Handheld arthropod detection device

ABSTRACT

Various embodiments include systems and methods of arthropod detection using an electronic arthropod detection device. The electronic arthropod detection device may scan a surface or a subject using a terahertz sensor that is sensitive to a terahertz band of electromagnetic radiation to detect the presence or likely presence of an arthropod in a region of interest (ROI). A camera sensitive to a visible band of electromagnetic radiation captures at least one image and provides the image(s) to an object detection model in response to determining that an arthropod is or is likely present in the ROI. A processor may initiate an arthropod detected procedure in response to detecting an arthropod in the ROI.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/978,964 entitled “Handheld Arthropod Detection Device” filed May 14,2018, which is a continuation-in-part of U.S. patent application Ser.No. 15/673,389 entitled “Handheld Arthropod Detection Device” filed Aug.9, 2017, which claims the benefit of priority to U.S. ProvisionalApplication No. 62/373,578 entitled “The Handheld Arthropod DetectionWand” filed Aug. 11, 2016, the entire contents of all of which arehereby incorporated by reference for all purposes.

BACKGROUND OF THE INVENTION

Many arthropods are responsible for disease transmission and thus may bea major health concern. Parasitic arthropods, including arachnids suchas ticks and mites and insects such as bedbugs, lice, and fleas, may bemore likely to transmit disease. Early detection and removal ofarthropods from a host body or various surfaces may reduce bothinfestation and disease transmission attributable to parasiticarthropods.

SUMMARY

Various embodiments include an arthropod detection device configured todetect, identify, and alert a user to the presence of a variety ofarthropods. A method of arthropod detection using an electronicarthropod detection device may include scanning a subject using aterahertz band sensor of the electronic arthropod detection device toidentify a region of interest (ROI) corresponding to an arthropod, andinitiating an arthropod detected procedure in response to identifying anROI corresponding to an arthropod. Some embodiments may further includecapturing at least one image of the ROI by an imaging sensor sensitiveto a visible band of electromagnetic radiation in response todetermining that an arthropod is or is likely present in the ROI.

In some embodiments, initiating the arthropod detected procedure inresponse to receiving the result from the object detection model mayinclude one or more of displaying an indication that an arthropod hasbeen detected, generating an audio indicator corresponding to thedetected arthropod, displaying an image of the detected arthropod,displaying instructions associated with how to remove the arthropod fromthe subject or the surface, and displaying medical follow-upinformation.

Some embodiments may further include receiving an indication associatedwith a type of arthropod to be detected, and retrieving a first objectdetection model from a plurality of object detection models in responseto receiving the indication associated the type of arthropod to bedetected. The first object detection model may be used to determinewhether an arthropod is detected within the identification area of theat least one image in the visible range. The plurality of objectdetection models may correspond to one or more of a tick, a bedbug, amite, lice, a flea, and a combination thereof.

Various embodiments may include an electronic arthropod detectiondevice. The electronic arthropod detection device may include aterahertz band sensor, an image sensor, a memory, and a processorcoupled to the memory, the terahertz band sensor and the image sensor.The processor may be configured with processor-executable instructionsto perform operations including scanning a subject using the terahertzband sensor to detect a region of interest (ROI) corresponding to anarthropod, and initiating an arthropod detected procedure in response toreceiving a result from the object detection model that indicates thatan arthropod is detected within the identification area of the at leastone image. The terahertz band sensor and the image sensor may beincluded in an arthropod detection module, and the arthropod detectionmodule may be separate from the processor.

In some embodiments, the electronic arthropod detection device mayfurther include a display, and a speaker. The processor may beconfigured with processor-executable instructions to perform operationssuch that initiating the arthropod detected procedure in response todetermining that an arthropod is detected in the ROI may include one ormore of displaying an indication on the display that an arthropod hasbeen detected, communicating an audio indicator via the speakercorresponding to the detected arthropod, displaying on the display animage of the detected arthropod, displaying on the display instructionsassociated with how to remove the arthropod from the subject or thesurface, and displaying on the display medical follow-up information.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and constitutepart of this specification, illustrate exemplary embodiments, andtogether with the general description given above and the detaileddescription given below, serve to explain the features of the variousembodiments.

FIG. 1 is a component block diagram of a communication system suitablefor use with various embodiments.

FIGS. 2-3 are images of exemplary arthropods that may be detected by adevice according to various embodiments.

FIG. 4 is an image of a tick captured using an exemplary UVA sensor.

FIG. 5A is a component block diagram of an electronic arthropoddetection device including an IR sensor according to variousembodiments.

FIG. 5B is a component block diagram of an electronic arthropoddetection device including a mm-wave/THz radar or far infrared sensoraccording to various embodiments.

FIG. 6 is a process flow diagram illustrating a method of arthropoddetection according to various embodiments.

FIG. 7 is a process flow diagram illustrating a method of performingobject detection according to some embodiments.

FIG. 8 is a process flow diagram illustrating another method ofperforming object detection according to some embodiments.

FIG. 9 are images captured during arthropod detection methods accordingto various embodiments.

FIG. 10 is a process flow diagram illustrating a method of updating anobject detection model according to various embodiments.

FIGS. 11A and 11B are perspective views of an arthropod detection deviceaccording to various embodiments.

FIGS. 12 and 13 are views of another arthropod detection deviceaccording to various embodiments.

FIGS. 14A-14D, 15 and 16 are views of another arthropod detection deviceaccording to various embodiments.

FIG. 17 is a component block diagram of a wireless communication devicesuitable for use with various embodiments.

FIG. 18 is a component block diagram of a server device suitable for usewith various embodiments.

FIG. 19 is a component block diagram of another electronic arthropoddetection device according to some embodiments.

FIG. 20 is a process flow diagram illustrating a method of performingobject detection using an electronic arthropod detection deviceincluding a mm-wave/THz radar or far infrared sensor according to someembodiments.

FIG. 21 is a process flow diagram illustrating another method ofperforming object detection using an electronic arthropod detectiondevice including a mm-wave/THz radar or far infrared sensor according tosome embodiments.

FIG. 22 is a component block diagram of another electronic arthropoddetection device including a mm-wave/THz radar or far infrared sensoraccording to some embodiments.

DETAILED DESCRIPTION

Various embodiments will be described in detail with reference to theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.References made to particular examples and embodiments are forillustrative purposes, and are not intended to limit the scope of thevarious embodiments or the claims.

Various embodiments may include methods and electronic devicesconfigured to implement methods for detecting a presence of one or morearthropods. For example, an electronic arthropod detection device may bea hand-held electronic arthropod detection device configured to becapable of scanning a surface or a subject (e.g., the skin of a human orfur of an animal), detecting and identifying arthropods, and/or alertinga user when an arthropod is detected. In various embodiments, theelectronic arthropod detection device may be a standalone device, or asmartphone or another wireless device coupled to a scanning system orcomponent.

The various embodiments may include an arthropod detection device orsystem configured to detect, recognize, identify, and/or alert a user tothe presence of a variety of arthropods (e.g., ticks, mites, bedbugs,lice, fleas, etc.). Various embodiments may use imaging and/or acousticdetection modalities to detect an arthropod. In some embodiments, thehandheld arthropod detection device may be configured with only one ofthe imaging modalities described herein such as high resolution digitalphotography along with image recognition software. In some embodiments,the handheld arthropod detection device may be configured with multipleimaging modalities in combination with image recognition software inwhich the user may select which imaging modality or modalities to use.In some embodiments, the handheld arthropod detection device may beconfigured with multiple imaging modalities and image recognitionsoftware executing on a processor that is configured to automaticallyselect the most appropriate imaging modality or modalities to use fordetecting any or a particular type of arthropod under circumstances ofuse. In some embodiments, the handheld arthropod detection device mayinclude multiple imaging modalities and a processor configured withimage recognition software and software configured to combine data fromtwo or more of the imaging modalities to enhance detection ofarthropods. As arthropods may be so small they are difficult to see withthe unaided human eye, the arthropod detection device may use multipleimaging modalities, including high-resolution imaging sensors for thescanning and detection of the arthropods. In addition, arthropoddetection device may be configured with an acoustic detection modalitydescribed herein such as one or more acoustic sound receivers incombination with signal processing software. Various embodiments of thehandheld arthropod detection device may use any or all of theseconfigurations as described in more detail below.

The arthropod detection device may include an electronic arthropoddetection device that is configured to be capable of scanning a subject(e.g., a human or animal body) or a surface and alerting a user when anarthropod is detected. The arthropod detection device may be implementedas a handheld arthropod detection wand or as an arthropod detectionelement that can be connected to a wand or a handle to enable users toscan all surfaces of their bodies, including difficult to view areassuch as the scalp, back, buttocks, and perineal area or scan anotherindividual, animal or surface. For example, the arthropod detectiondevice may include a handle or other attachment configured to removablycouple a portion of the arthropod detection device to a smartphone orother mobile communication device. In some embodiments, the arthropoddetection device may include a handle having one or more elements thatallow an imaging component of the arthropod detection device totranslate and/or rotate with up to six degrees of freedom. In someembodiments, the handle may include a telescoping rod and/or one or moremechanical bearings (e.g., hinges, ball bearings, etc.) to dynamicallyposition one or more imaging components with respect to the subject orsurface to be scanned for arthropods.

Using more than one type of imaging or detecting modality (e.g., IRimaging, visible light imaging, far infrared sensing/imaging,mm-wave/THz radar detection, etc.) may increase the probability that thearthropod detection device will detect an arthropod. The one or moretypes of imaging modalities may be used in combination with more thanone type of image analysis model, which may be stored in the memory ofthe arthropod detection device and/or at a server that may communicatewith the arthropod detection device (e.g., via the Internet and awireless communication network). The user may be able to select one ormore imaging modalities or choose the automatic mode that enables theelectronic arthropod detection device to select one or more modalitiesas it “searches” for the presence of the arthropod(s). The one or moretypes of imaging analysis models for detecting arthropods withincaptured images may be generated by the server and the server maytransmit the image analysis models to the arthropod detection device.

The imaging modalities implemented by the arthropod detection device mayinclude one or more of the following: high resolution digital imaging,high resolution thermal imaging (e.g., an infrared (IR) camera), andultraviolet (UV) light set at specified wavelengths to enhance thevisibility of the arthropods, and a high resolution UV imager. In afurther embodiment, the imaging modalities implemented by the arthropoddetection device may include a mm-wave or THz radar or far infraredsensor that may function to identify an ROI for further investigationusing one or more of high resolution digital imaging, high resolutionthermal imaging (e.g., an infrared (IR) camera), and ultraviolet (UV)light set at specified wavelengths to enhance the visibility of thearthropods, and a high-resolution UV imager.

A mm-wave/THz radar or far infrared sensor may include any of a varietyof sensors, including imaging sensors, configured to operate in theelectromagnetic spectrum with wavelengths long enough to penetrate humanand animal hair but short enough to reflect off of an arthropod ofinterest. In some circumstances, human hair, with follicle diameters of0.05-0.08 mm, and animal fur, with follicle diameters of 0.025-0.03 mm,may absorb IR light in the wavelengths detected by typical IR cameras.While small at just 1-2 mm in diameter, nymph ticks are substantiallylarger than animal and human hair follicles. Thus, a mm-wave or THzradar or far infrared sensor emitting and sensitive to wavelengthslonger than about 0.10 mm may be used to penetrate hair and fur todetect the presence of arthropods and thus identify an ROI forinspection using higher resolution imaging. Commercial mm-wave imagingradars are now commercially available for a variety of applications. Forexample, Vayyar Imaging (see vayyar.com) markets a 72 pixel imagingmm-wave radar chip covering 3 GHz to 81 GHz radio frequency (RF) bands.81 GHz radar will pass through human and animal fur and thus could beused detect the presence of arthropods down to about 4 mm in diameter.Higher frequency RF energy, such as in the range of 300 GHz or higher,should be able to detect the presence of nymphs within dense fur. Thisfrequency range, also known as terahertz (THz) or submillimeterradiation, has a wavelength of less than a millimeter, and thus is ableto reflect off of arthropods within the size range of concern. Someembodiments include a THz emitter and receiver (e.g., an array of THzreceivers) to detect and image arthropods within hair or fur using THzradiation (referred to herein as a THz radar). Similar, some embodimentsuse far infrared radiation emitters and receivers (e.g., an array of farinfrared receivers) to detect and image arthropods within hair or furusing far infrared radiation.

The acoustic sound modalities implemented by the arthropod detectiondevice may detect sounds generated by one or more arthropods. In someembodiments, the acoustic sound modalities may passively detect feeding,chewing, moving, mating, and/or warning sounds generated by the one ormore arthropods. The arthropod detection device may include one or moreacoustic receivers such as a microphone, a transducer, an ultrasonicdetector, and a piezoelectric sensor. In further embodiments, theacoustic sound modalities may include an active mode in which ultrasoundis emitted and echoes are received by the one or more acoustic receiversto determine a separation distance between the arthropod detectiondevice and a surface being scanned (e.g., to guide a user in properlypositioning the device with respect to the surface). In some instances,an active mode may be used to identify regions for closer inspection bydetecting echoes from larger arthropods (e.g., large ticks hidden in furof a pet).

Image recognition software may be employed by the arthropod detectionsystem to “detect” the presence of the arthropod(s) through the analysisof one or more of the images (digital, thermal, UV enhanced).

When features characteristic of an arthropod species are detected by thearthropod detection device, one or more outputs of a sensor associatedwith each of the imaging modalities may be recorded and/or stored in thearthropod detection system. The images may automatically or selectivelybe recorded and stored in the arthropod detection device. The arthropoddetection device may allow for the storage and transmission of imagescaptured during scanning for record keeping and further analysis. Theimages may be transferable to another electronic device such as asmartphone, mobile communication device, a computer, and/or a server,which together with the arthropod detection device may function as anarthropod detection system. In addition, the arthropod detection devicemay generate a real-time auditory and/or visual indication to alert auser that an arthropod has been detected.

Thus, the arthropod detection device may significantly enhance detectionof the presence of arthropods to allow a user to remove and/or eradicatea detected arthropod in a timely manner thus greatly reducing the riskof disease transmission, skin irritation, and/or discomfort.

Conditions where the arthropod detection device may detect arthropodsotherwise difficult or impossible to see with the unaided eye mayinclude beneath clothing, in areas obscured by hair or animal fur,arthropods too small to be readily seen, arthropods that blend in withsurrounding color tones, in places with poor lighting, and in regions ofthe body or surfaces difficult to access and visualize. An auditoryand/or visual alert may signal the user when an arthropod has beendetected. Thus, the arthropod detection devices and systems of thevarious embodiments may allow a user to scan his or her own body, otherindividuals, animals, and/or various surfaces for the presence ofarthropods.

The arthropod detection device may provide an effective, simple, andreliable means of detecting the presence of arthropods on a host subjectand/or in the environment. The arthropod detection device may be moreeffective than the unaided eye at detecting these small organisms whichare frequently obscured by hair and clothing.

Early and reliable detection of arthropods, many of which serve as majordisease vectors, may reduce health risks posed by parasitic organismsboth domestically and internationally.

The term “wireless communication device” is used herein to refer to anydevice that may use radio frequency (RF) communications to communicatewith another device, for example, as a participant in a wirelesscommunication network. A wireless communication device implementingvarious embodiments may include any one or all of mobile computingdevices, laptop computers, tablet computers, cellular telephones,smartphones, personal or mobile multi-media players, personal dataassistants (PDAs), smartbooks, palmtop computers, wireless electronicmail receivers, multimedia Internet enabled cellular telephones,wireless gaming systems and controllers, smart appliances includingtelevisions, set top boxes, kitchen appliances, lights and lightingsystems, smart electricity meters, air conditioning/HVAC systems,thermostats, building security systems including door and window locks,vehicular entertainment systems, vehicular diagnostic and monitoringsystems, unmanned and/or semi-autonomous aerial vehicles, automobiles,sensors, machine-to-machine devices, and similar devices that include aprogrammable processor, memory, and/or circuitry for establishingwireless communication pathways and transmitting/receiving data viawireless communication networks. Various embodiments may be particularlyuseful in mobile computing and mobile communication devices, such assmart phones, tablet computers and other portable computing platformsthat are easily transported to locations where rogue access points maylurk.

The tell is “component,” “module,” “system,” and the like as used hereinare intended to include a computer-related entity, such as, but notlimited to, hardware, firmware, a combination of hardware and software,software, or software in execution, which are configured to performparticular operations or functions. For example, a component may be, butis not limited to, a process running on a processor, a processor, anobject, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on acommunication device and the communication device may be referred to asa component. One or more components may reside within a process and/orthread of execution and a component may be localized on one processor orcore and/or distributed between two or more processors or cores. Inaddition, these components may execute from various non-transitorycomputer readable media having various instructions and/or datastructures stored thereon. Components may communicate by way of localand/or remote processes, function or procedure calls, electronicsignals, data packets, memory read/writes, and other known computer,processor, and/or process related communication methodologies.

Various embodiments may be implemented within a variety of communicationsystems 100, an example of which is illustrated in FIG. 1. Thecommunication system 100 may include an arthropod detection device 102,communication network 110, and a server 114.

The arthropod detection device 102 may include an electronic arthropoddevice configured to scan, detect, identify, and/or generate an alert toindicate whether an invertebrate 106 is present on a subject or asurface. While FIG. 1 illustrates the invertebrate 106 as a tick, theinvertebrate 106 may be any invertebrate such as a parasitic arthropodor insect including ticks, mites, bedbugs, lice, fleas, etc. The subjectmay include a human host or an animal host such as a dog, a cat, ahorse, a deer, etc. The surface be any surface such as an environmentalsurface (e.g., a tree, a grass, a bush, a rock, etc.), a woven surface(e.g., fabric, clothing, etc.), etc. The arthropod detection device 102may include a processor configured with processor-executableinstructions to implement one or more image analysis techniques to scan,detect, and/or identify the invertebrate 106.

The arthropod detection device 102 may include one or more communicationinterfaces configured to allow the arthropod detection device 102 towirelessly communicate with the communication network 110 and/or anotherelectronic device via a communication link 108. For example, the one ormore communication interfaces of the arthropod detection device 102 mayimplement a relatively short-range wireless communication protocol suchas Wi-Fi, ZigBee, Bluetooth, or IEEE 802.11, or a long-rage wirelesscommunication protocol such as a cellular protocol including 3GPP LongTerm Evolution (LTE), Global System for Mobility (GSM), Code DivisionMultiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA),Worldwide Interoperability for Microwave Access (WiMAX), Time DivisionMultiple Access (TDMA), and other mobile telephony communicationtechnologies. Alternatively, the arthropod detection device 102 mayinclude one or more ports configured to allow the arthropod detectiondevice 102 to connect with the communication network 110 and/or anotherelectronic device via a wired cable or plug. The arthropod detectiondevice 102 also may be a stand-alone unit that does not include acommunication interface.

In some embodiments, when the arthropod detection device 102 includes asmartphone or other mobile communication device, the arthropod detectiondevice 102 may further include another communication interface to allowthe smartphone to communicate with another element of the arthropoddetection device 102.

The server 114 may generate one or more arthropod identification modelsused to identify whether an arthropod is present during detection and/ora type of arthropod when an arthropod is present. The arthropodidentification models may be generated using crowd sourcing solutions,machine learning algorithms, etc. For example, the server 114 may usemachine learning techniques to intelligently and efficiently identifydifferent arthropods using a plurality of images. The server 114 mayreceive a plurality of images that correspond to each type of imagingtechnique implemented by the arthropod detection device 102.

To generate one or more arthropod identification modules using machinelearning techniques, the server 114 may evaluate a plurality of imagesand classify each of the images based on one or more features,characteristics, and/or aspects associated with a subject of each of theimages. For example, the server 114 may analyze a large number of imagesto train an image recognition model to determine a type of arthropod, aspecies, a sex, a size, a shape, a life stage, whether the arthropod isengorged or not, the extent of engorgement, a temperature signature ofthe arthropod, a temperature signature of a subject, etc. The server 114may also extract information from each image to determine a date and/ortime associated with when the image was captured, a geographic locationof where the image was captured, etc.

In some embodiments, the server 114 may also receive sound recordings ofarthropods of various species and generate a model of recognizingspecies or identifying locations of for image scanning based on receivedsounds. For example, the server 114 may analyze a large number of soundrecordings in conjunction with images to train a sound recognition modelto determine a type of arthropod, a species, a sex, a size, a shape, alife stage, etc. The server 114 may also extract information from eachsound recording to determine a date and/or time associated with when thesound was captured, a geographic location of where the sound wascaptured, etc.

In some embodiments, the server 114 may also receive ultrasound echodata obtained from arthropod detection devices 102 using active acousticmodalities while scanning arthropods of various species and generate amodel for determining locations of interest for image scanning based onreceived ultrasound echoes.

The server 114 may generate one or more arthropod identification models.For example, the server 114 may generate an arthropod identificationmodel for each type of arthropod that may be detected using thearthropod detection device 102. The server 114 may also generate anarthropod identification model that includes a plurality of differenttypes of arthropods to allow the arthropod detection device 102 toidentify more than one type of arthropod at a time. Each of thearthropod identification models may be based on a plurality of images, aplurality of sounds, or a combination thereof.

The arthropod detection device 102 may be in communication with theserver 114 via the communication network 110. For example, the server114 may transmit one or more arthropod detection models to the arthropoddetection device 102 over communication link 112. In addition, thearthropod detection device 102 may transmit one or more images capturedat the arthropod detection device 102 to the server 114 using thecommunication link 108. The server 114 may collect images of arthropodsprovided by a large number of arthropod detection devices 102, togenerate a “crowd-sourced” database, and use such collected images tofurther refine arthropod detection models that may be sent back toarthropod detection devices 102 in periodic updates. Such updates may bevia “over-the-air” updates communicated using the communication link108.

In some embodiments, the arthropod detection device 102 may transmitimages detected at the arthropod detection device 102 over thecommunication network 110 to a device associated with an arthropodspecialist and/or a physician (not illustrated). In some embodiments,the arthropod specialist may identify characteristics in the imagesreceived from the arthropod detection device 102 and/or classify theimages according to the identified characteristics. The identifiedcharacteristics and/or classifications may be used to update the objectdetection model. In another embodiment, the arthropod specialist and/orthe physician may use the images received from the arthropod detectiondevice 102 to further classify the arthropod, determine whether thearthropod may be a potential vector for disease, identify a potentialdisease associated with the arthropod, and/or assess risk of diseasetransmission attributable to the arthropod. For example, the images maybe captured by the arthropod detection device 102. The arthropoddetection device 102 may send the images to the physician and/or thearthropod specialist. The physician may review the images for diagnosispurposes (e.g., to confirm exposure to a potentiallydisease-transmitting arthropod). Alternatively, the physician mayforward the images to the arthropod specialist for the arthropodspecialist to provide an opinion as to an arthropod type includingspecies and other characteristics that would influence the risk ofdisease transmission. In embodiments including acoustic modalities,sounds or echo data may also be shared with the physician and/or thearthropod specialist.

FIGS. 2-3 are images of arthropods as may be imaged by arthropoddetection devices 102 and analyzed using arthropod detection modelsaccording to various embodiments. FIG. 2 includes images of ticks, adust mite, and lice according to various embodiments. FIG. 3 includesimages of a bedbug and a flea.

Arachnids such as ticks are responsible for the transmission of numeroustick-borne illnesses that can result in significant morbidity andmortality. These diseases include Lyme disease, Rocky Mountain spottedfever, Babesiosis, Ehrlichiosis, and Colorado tick fever to name a few.Tick-borne illnesses are transmitted by a variety of tick species thatare identifiable by various markings and other features. The ability toidentify a tick in the earliest stages of attachment to the human oranimal body (ideally in <24 hours) may significantly reduce the risk ofinfection with Lyme disease and other tick-borne illnesses.

Cases of Lyme disease in humans have tripled over the past two decadesand are now estimated to number 300,000 annually in the US alone. Lymedisease is now the most common vector-borne disease in the northernhemisphere. Undiagnosed and untreated, approximately 60% will developchronic arthritis and 10-20% will develop debilitating neurological andcardiac disorders including dementia, peripheral neuropathies, andcardiac arrhythmias/conduction disorders. Lyme disease in pregnancy canlead to miscarriage and stillbirth. In canines, approximately 1 in 16will test positive for Lyme disease, also leading to chronicdebilitating conditions if left untreated.

Lyme disease is caused by the bacterium Borrelia Burgdorferi. Twospecies of tick are known vectors of the disease. The Blacklegged tick(deer tick or Ixodes Scapularis) is endemic to the northeastern,midatlantic and north-central United States. The Western Blackleggedtick (Ixodes Pacificus) is endemic to the Pacific coast.

As illustrated in FIG. 2, these tick species may go through four stagesof development during their lifecycle: egg, larva 202, nymph 204, andadult (male 206 or female 208). Lyme and other tick-borne illness aretransmitted during the nymph and adult stages. These stages aredistinguishable by highly characteristic features including size, numberof segmented legs, and dorsal shield markings. In some embodiments, theserver 114 and/or the arthropod detection device 102 may use thesecharacteristics as key features used in arthropod detection models toidentify an invertebrate 106 within an image (e.g., a high-resolutionvisible light image) as a tick as well as a specific species (e.g., deertick, etc.).

The CDC (Centers for Disease Control) has outlined strategies forreducing the risk of human exposure to Lyme disease and other tick-borneillness. These strategies include avoidance of tick habitats (grassywooded areas), efforts to reduce tick habitats in residential areasthrough landscaping and keeping grass well cut, use of tick repellantson humans and dogs (application of products with 20-30% DEET to the skinand 0.5% permethrin to clothing are recommended for humans), and visualinspection for tick detection and removal.

Since it is virtually impossible to eliminate the risk of tick exposure,visual inspection of oneself and others is critical to Lyme disease andother tick-borne illness prevention. Unfortunately, ticks are oftendifficult to detect with the unaided eye due to multiple factors, thusincreasing the risk of contracting Lyme disease. First, the majority ofcases of Lyme disease are transmitted by ticks during the nymph stagewhen they are quite small (<2 mm) and therefore difficult to see.Secondly, ticks tend to attach to areas along the scalp, groin, andaxilla where they may be obscured by hair. Also, ticks can attachthemselves to the posterior neck, lower back, behind the ears, and otherareas of the body often difficult if not impossible to see wheninspecting oneself In canines, visual inspection is particularlydifficult due to their thick fur. Finally, ticks secrete a naturalanesthetic when attaching to the host making detection even moredifficult.

Aside from an individual performing a visual scan, there are currentlyno other methods for aiding in the detection of ticks, which haveattached to their hosts. A device that can scan and immediately alertthe individual to the presence of one or multiple ticks on a host istherefore desperately needed so that ticks can be located and quicklyremoved from the host before disease transmission.

It is desirable to also detect a presence of other arachnids such asmites. The female Sarcoptes scabiei (mite) is 0.3 to 0.45 mm long and0.25-0.35 mm wide. Also known as the itch mite, this parasitic arthropodburrows into the skin of a human host and lays eggs, resulting inScabies, a skin disease associated with extreme pruritus, skinirritation/disruption and secondary bacterial skin infections. Skinrash, pustules, blisters and nodules can form as a result of allergiesto the mites' eggs and feces. As illustrated in FIG. 2, a characteristicappearance of a mite 210 may include four pairs of legs, an oval shellwith a flat anterior surface and convex posterior surface, covered withtriangular spines. Mites can also infest domesticated dogs and cats aswell as pigs and other non-domesticated mammals Scabies is readilytransmitted by contact between persons. Left untreated, scabies willprogress and spread to involve new areas of the skin. Detection iscritical to both effective treatment and prevention of further spread ofthe parasite.

By implementing different arthropod detection models (e.g., provided bya server and stored in memory), the arthropod detection device 102 ofvarious embodiments may be configured to detect parasitic insects suchas lice. Lice may have characteristic identifying features and range insize from 1.1 to 3.6 mm in length and may be transmitted through closeperson-to-person contact. Types include head lice 212, body lice 214,and pubic lice 216. Head and pubic lice do not transmit disease but cancause skin irritation and pruritus. Body lice (Pediculus humanus) areresponsible for disease transmission including epidemic typhus(rickettsia prorazekkii), epidemic relapsing fever (Borreliarecurrentus), and trench fever (Bartonella quintana). Infections aretransmitted via a louse bite or inoculation with louse feces.

Adult head lice 212 may be 2.1-3.3 mm in length. Head lice 212 mayinfest the head and/or neck of a host and attach eggs to the base of ahair shaft. Lice are unable to hop or fly and instead move by crawling.Adult body lice 214 may be 2.3-3.6 mm in length. Body lice 214 may liveand lay eggs on clothing and only move to the skin of a host to feed.Adult pubic lice 216 may be 1.1-1.8 mm in length. Typically, pubic lice216 may be found attached to hair in the pubic area of a host. However,sometimes pubic lice 216 may be found on coarse hair elsewhere on thebody of the host such as eyebrows, eyelashes, beard, mustache, chest,armpits, etc.

By implementing different arthropod detection models (e.g., provided bya server and stored in memory), the arthropod detection device 102 ofvarious embodiments may be configured to detect bedbugs. Bedbugs are asignificant cause of pruritic skin lesions, are emerging as a potentialvector for disease, and can result in significant economic hardship forfamilies and businesses alike. As illustrated in FIG. 3, bedbugs 302 arewingless parasitic insects with 6 legs and other characteristic featuresmaking them readily identifiable. For example, bedbugs may includecoloring ranges from white to brown to rusty and can measure up to 0.5cm in size. The common bedbug (Cimex lectularius) feeds on human bloodfor 3 to 10 minutes before leaving its host. It then retreats to avariety of areas including bedding, mattresses, box springs, curtains,carpets, and clothing. Bites from bedbugs are initially painless butthen cause itchy welts. Bedbugs are not generally believed to transmitdisease. However, recent research suggests they may develop over timeinto a vector for Trypanosoma cruzi, a protozoan parasite responsiblefor Chagas disease. Currently, Chagas disease is prevalent in Mexico,Central America and South America where its main vector is the insectTriatoninae (kissing bugs). Chronic infection can lead to significantmorbidity and mortality including cardiomyopathy and heart failure.

Diagnosis of the skin lesions caused by bedbugs is dependent on thespecific isolation and identification of the insect. Since the parasiteonly spends a brief time on its host while feeding, the scanning ofvarious surfaces, especially fabrics, is necessary to isolate andidentify bedbugs. Since they can live up to a year without feeding athorough and aggressive approach is necessary in order to eradicate theparasite. A highly effective means of screening for these parasites isessential to prevent infestation.

By implementing different arthropod detection models (e.g., provided bya server and stored in memory), the arthropod detection device 102 maybe configured to detect fleas. As illustrated in FIG. 3, fleas havecharacteristic features including highly sclerotized bodies and measure2-10 mm in size. Numerous varieties of fleas exist including theCtenocephalides felis (cat flea), Ctenocephalides canis (dog flea),Tunga Penetrans (sand flea or jigger) and Pulex Irritans (human flea).

Fleas are responsible for the transmission of numerous flea-bornediseases in both humans and animals. The Plague (Y. pestis), is anexample of a zoonotic disease transmitted from rodents to humans byfleas. The Plague is considered a re-emerging disease and a majorinternational health threat. Other examples of zoonotic diseasestransmissible by fleas include rickettsial infections such as murinetyphus (endemic typhus, rickettsia typhus), rural epidemic typhus(rickettsia prorazekkii), and flea-borne spotted fever (Rickettsiafelis). Fleas transmit these and other diseases through both theregurgitation of blood meals and through their feces to the host. Fleascan also cause significant skin irritation, rashes, and discomfort.

As with tick and mite control, bedbug, lice, and flea control strategiesprimarily focus on insecticides and repellants, as well as avoidance.Other than visual inspection with the unaided eye, there are noroutinely employed techniques to assist in the early detection of theseparasites on their hosts or on surfaces. Early detection and eradicationis critical to preventing disease transmission and the development ofrashes, skin irritation and discomfort in the host human and/or domesticanimal.

FIG. 4 is an image of a tick captured using an exemplary UVA sensoraccording to various embodiments. In addition to physicalcharacteristics, alternative imaging techniques may be used to identifyadditional characteristics associated with arthropods. For example, asillustrated in FIG. 4, when a tick is exposed to light in the UVAspectrum (10 nm to 400 nm), one or more joints of the segmented legs mayfluoresce at a different wavelength than the rest of the tick.

FIG. 5A is a component block diagram of an electronic arthropoddetection device 502 according to various embodiments. In someembodiments, the electronic arthropod detection device 502 may be thearthropod detection device 102.

The electronic arthropod detection device 502 may include an opticalimage capture system 504, a thermal image capture system 506, a memory510, a processor 516, an image processor 518, and an object detectionmodule 520. The electronic arthropod detection device 502 may optionallyinclude an ultraviolet (UV) image capture system 508, an input/outputinterface 512, one or more acoustic sound receivers 514, a signalprocessor 522, or other sensors (not illustrated). While the imageprocessor 518, the object detection module 520, and the signal processor522 are illustrated in FIG. 5 as being within the processor 516, theimage processor 518, the object detection module 520, and/or the signalprocessor 522 may alternatively be separate modules in communicationwith the processor 516.

The optical image capture system 504 may include one or more opticalimage sensors configured to capture an image of an object when theobject is in the field of view of the optical image capture system 504.The thermal image capture system 506 may include one or more thermalimaging sensors configured to capture a thermal image of an object whenthe object is in the field of view of the thermal imaging sensor.Likewise, if the UV image capture system 508 is included in theelectronic arthropod detection device 502, the UV image capture system508 may include one or more UV image sensors. In some embodiments, theelectronic arthropod detection device 502 may illuminate the subjectwith UV light (e.g., from a UV light source) to enhance the visibilityand/or detection of arthropods such as mites, lice, fleas, bedbugs, etc.

In some embodiments, each of the optical image capture system 504, thethermal image capture system 506, and the UV image capture system 508,may include a lens assembly or camera. Alternatively, one or more of theoptical image capture system 504, the thermal image capture system 506,and the UV image capture system 508 may share a lens assembly or camera

The memory 510 may store instructions and/or data. For example, a camerasoftware application may be stored in the memory 510, such that when thecamera application is executed, images of one or more objects locatedwithin a field of view of the image sensors corresponding to the opticalimage capture system 504, the thermal image capture system 506, and theUV image capture system 508, images are captured by the correspondingimage sensors. In some configurations, the images may be captured inrapid succession at a relatively high frame rate. The one or more imagesobtained by the electronic arthropod detection device 502 may includeone or more image frames, video frame, and/or one or more still images.

In addition, the memory 510 may store images captured by the imagesensors of the optical image capture system 504, the thermal imagecapture system 506, and/or instruction codes for performing operationsby the processor 516. The memory 510 may be any electronic componentcapable of storing electronic information. The memory 116 may beembodied as a random access memory (RAM), read-only memory (ROM),magnetic disk storage media, optical storage media, flash memory devicesin RAM, on-board memory included with the processor, EPROM memory,EEPROM memory, registers, and so forth, including combinations thereof.

The data and instructions stored in the memory 510 may includeinstructions executable by the processor 516 to perform one or more ofthe methods described herein. Executing the instructions may involve theuse of the data that is stored in the memory 510. When the processor 516executes the instructions, various portions of the instructions may beloaded onto the processor 516, and various pieces of data may be loadedonto the processor. The instructions executed by the processor 516 mayinclude analyzing images using one or more arthropod detection modelsthat may be store in the memory 510 or within an object detection module520.

The processor 516 may be coupled to (e.g., in electronic communicationwith) the optical image capture system 504, the thermal image capturesystem 506, the memory 510, the ultraviolet (UV) image capture system508, the input/output interface 512, or other sensors (not illustrated).The processor 516 may be a general-purpose single-chip or multi-chipmicroprocessor (e.g., an ARM), a special-purpose microprocessor (e.g., adigital signal processor (DSP)), a microcontroller, a programmable gatearray, etc. The processor 516 may be referred to as a central processingunit (CPU). Although a single processor 516 is illustrated in FIG. 5, inan alternative configuration, a combination of processors (e.g., an ARMand a DSP) could be used. The processor 516 may be configured toimplement the methods disclosed herein, which will be explained indetail below, to determine whether a presence of an arthropod isdetected.

The image processor 518 may be configured to perform various imageprocessing techniques on various images captured by the electronicarthropod detection device 502. In various embodiments, the imageprocessor 518 may be configured to perform filtering, demosaicing, noisereduction, and/or image sharpening to one or more of the images capturedby the electronic arthropod detection device 502. In addition, the imageprocessor 518 may identify objects or areas or regions of interestwithin one or more images captured by the electronic arthropod detectiondevice 502.

The object detection module 520 may be configured to store one or morearthropod identification models. The one or more arthropodidentification models may include information associated withcharacteristics that may allow the electronic arthropod detection device502 may use the one or more arthropod identification models to determinewhether an arthropod is detected within an image captured by theelectronic arthropod detection device 502 and/or to determine a type ofarthropod detected within an image captured by the electronic arthropoddetection device 502. The arthropod identification models may begenerated, such as by a server (e.g., 114 of FIG. 1) using any type ofimages. For example, the arthropod identification models may be based onoptical images, IR images, UV images, or a combination thereof. In someembodiments, if the information being sent to the object detectionmodule 520 is from an IR image, the arthropod identification model maybe generated using a plurality of IR images. Likewise, if theinformation being sent to the object detection module 520 is from anoptical image, the arthropod identification model may be generated usinga plurality of optical images.

The optional input/output (I/O) interface 512 may be configured to allowa user to interact with the electronic arthropod detection device 502and/or receive feedback from the electronic arthropod detection device502. The I/O interface 512 may include one or more of a user interface,an input device such as a key, button, toggle, dial, a microphone, etc.,an output device such as one or more speakers and/or a motor to generatevibrations, etc., a display, a touchscreen, etc.

The one or more optional acoustic sound receivers 514 may be configuredto detect a soundwave generated by an arthropod within a scan area. Forexample, arthropods within a scan area may generate incidental soundssuch as feeding, chewing, and/or moving noises or communication soundssuch as mating sounds and/or warning sounds. Incidental sounds of anarthropod may be softer and harder to detect than communication sounds.

The one or more optional acoustic sound receivers 514 may be one or moreof a microphone, a transducer, an ultrasonic detector, and apiezoelectric sensor. In some embodiments, the one or more optionalacoustic sound receivers 514 may further include an amplifier to amplifyan electric signal generated by the microphone, the transducer, theultrasonic detector, and/or the piezoelectric sensor to a level that issufficient for detection.

The one or more optional acoustic sound receivers 514 may detectacoustic sound waves within the same frequency range and/or differentfrequency ranges. For example, a first acoustic sound receiver and/or asecond acoustic sound receiver may be configured to detect soundwaveswithin a human audible range (e.g., 20 Hz to 20 kHz), an ultrasonicrange (e.g., 20 kHz to several gigahertz), and/or a portion thereof. Insome embodiments, since incidental sounds of an arthropod may be moredifficult to detect than communication sounds, the first acoustic soundreceiver may be tuned to detect incidental sounds of an arthropod andthe second acoustic sound receiver may be tuned to detect thecommunication sounds of the arthropod.

The optional signal processor 522 may be in communication with the oneor more acoustic sound receivers 514. The optional signal processor 522may be configured to minimize and/or filter out undesirable noise orinterference detected by the one or more optional acoustic soundreceivers 514. For example, the signal processor 522 may filter outsound waves outside of a predetermined frequency value. Thepredetermined frequency value may be a single value or a range offrequency values. In some embodiments, the predetermined frequency valuemay be defined for a particular arthropod species. Alternatively, thepredetermined frequency value may be defined for different stages ofdevelopment and/or gender for a particular arthropod species as well asfor different species. For example, the predetermined frequency valueassociated with a tick larva may be different from an adult tick.Alternatively, the predetermined frequency value of an adult male tickmay be different from the predetermined frequency value of an adultfemale tick. Moreover, the predetermined frequency value of a bedbug maybe different from a mite, etc.

In some embodiments, arthropod acoustic signals may be digitized and/orrecorded using the signal processor 522. The acoustic signals may beidentified using an auditory recognition API that uses a large arthropodsound database and classification algorithms.

The acoustic sound modalities may be configured to locate a region ofinterest associated with an area being scanned by the arthropoddetection device. For example, one or more acoustic sound modalities maybe implemented to perform an initial scan on the area of a subject orsurface. The result of the one or more acoustic sound modalities may beused to perform additional imaging modalities to determine whether oneor more arthropods are detected within the area being scanned.

In some embodiments, the acoustic sound modalities may be used in tandemwith the imaging modalities. The acoustic sound modality may beimplemented to alert a user as to an area where a search using theimaging modalities should be focused. For example, one or more acousticsound modalities may be implemented when the arthropod detection deviceis scanned over a mattress in a hotel room. When an indication thatthere is a potential for an arthropod to be detected is generated basedon the results of the acoustic sound modality, the user may then focusthe arthropod detection device within an area of the mattress associatedwith the results of the acoustic modality such that the arthropoddetection device may perform imaging using IR, optical, and/or UVenhanced imaging.

Alternatively, the acoustic sound modalities may be used alone todetermine whether an arthropod is detected within the scanned area ofthe subject or surface.

In further embodiments, the acoustic sound modalities may include anactive mode in which ultrasound is emitted (e.g., from a piezoelectricemitter/sensor element) and echoes are received by the one or moreacoustic receivers 514 to determine a separation distance between thearthropod detection device and a surface being scanned (e.g., to guide auser in properly positioning the device with respect to the surface). Insome instances, an active mode may be used to identify regions forcloser inspection by detecting echoes from larger arthropods (e.g.,large ticks hidden in fur of a pet).

FIG. 5B is a component block diagram of an electronic arthropoddetection device 502 b according to a further embodiment. In someembodiments, the electronic arthropod detection device 502 b may be thearthropod detection device 102.

FIG. 6 illustrates a method 600 of arthropod detection according tovarious embodiments. With reference to FIGS. 1 and 5, the method 600 maybe implemented by one or more processors of the arthropod detectiondevice 102 and/or an electronic arthropod detection device 502 a or 502b (e.g., processor 516).

In optional block 602, the processor may receive an identification of atype of object to be detected. For example, a user may specify aparticular type of arthropod to be detected during a current detectionsession using the I/O interface 512. If a user has been outside in thewoods for a prolonged period of time, the user may select to detect forticks. If a user wants to scan sheets or a mattress, the user may selectto detect for bedbugs. Alternatively, two or more different types ofarthropods may be identified to be included in the scan. In someembodiments, if no selection regarding a type of object identificationis received, the processor may determine that a general scan is to beperformed to initially identify a general type of arthropod and then amore detailed scan may be performed after an initial arthropod isdetected.

In optional block 604, the processor may receive an identification of atype of object to be detected. For example, a user may specify whether ascan of a human subject, an animal subject, and/or a surface is desiredusing the I/O interface 512.

In optional block 606, the processor may determine a type of scan to beperformed based on the identification of the type of object received inblock 602 and/or the identification of the type of subject received inblock 604. For example, a scanning procedure for a human subject may bedifferent from a scanning procedure for an animal subject. In addition,a scanning procedure for a first type of arthropod may be different froma scanning procedure for a second type of arthropod.

In optional block 608, the processor may retrieve an object detectionmodel based on the determined type of scan to perform. In someembodiments, the processor may retrieve a particular object detectionmodel from the object detection module 520 from the memory 510corresponding to the type of scan to be performed. In other embodiments,the arthropod detection device 102 may request an object detection modelassociated with the determined type of scan to be performed from theserver 114 and the server 114 may transmit an object detection model tothe arthropod detection device 102 such as the most up-to-date objectdetection model. Alternatively, the server 114 may periodically transmitan object detection model to the arthropod detection device 102 atpredetermined time intervals.

In block 610, the processor may initiate a scan for arthropods. The scanmay be initiated in various ways such as receiving an input via the I/Ointerface 512 of the electronic arthropod detection device 502 a or 502b, or detecting a predetermined gesture using the electronic arthropoddetection device 502 (e.g., a scanning gesture, etc.).

In block 612, the processor may capture one or more images using theoptical image capture system 504, the thermal image capture system 506,and/or the UV image capture system 508. In some embodiments, theprocessor may capture two or more images simultaneously using two ormore different capturing systems or sensors of the electronic arthropoddetection device 502 a or 502 b. Alternatively, the processor maycapture two or more images using the same capturing system of theelectronic arthropod detection device 502 a or 502 b and/or capture twoor more images using different capturing systems of the electronicarthropod detection device 502 a or 502 b at different times.

The images may be captured within a predetermined distance from asubject. For example, the images may be captured at about three to sixinches from an inspection surface of the subject. The captured imagesmay have various resolutions. For example, the IR camera images may becaptured at a resolution of about 80×60 pixels or higher, and thevisible light camera images may be captured at a resolution of about1440×1080 pixels or higher. Increased sensitivity and accuracy of theelectronic arthropod detection device 502 a or 502 b may be achieved byincreasing the resolution of the imaging sensors. For example, the IRcamera images may be captured at a resolution of about 160×120 pixels,and the visible light camera images may be captured at a resolution ofabout 1440×1080 pixels. In some embodiments, the IR camera images may becaptured at a minimum resolution of 80×60 pixels, and the visible lightcamera images may be captured at a minimum resolution of 1440×1080pixels.

In block 614, the processor may perform object detection using one ormore of the images captured in block 612. For example, the imageprocessor 518 may identify one or more regions of interest in a firstcaptured image. A region of interest in an image may include one or morepixels that are determined to have a contrast value greater than acontrast value threshold. In some embodiments, the contrast valuethreshold may be a predetermined value or the contrast value thresholdmay be dynamically determined based on a contrast value of one or morepixels surrounding a selected pixel.

The resolution of the image sensors may influence the processing timeassociated with performing object detection. For example, increasing theresolution of the image sensors increases the amount of data that isprocessed, and thus the processing time. As processor speeds increase,the resolution of the imaging sensors may also be increased. However,increasing image resolution beyond a certain level may provide littlefurther recognition benefits. Therefore, in some embodiments, the imagescaptured in block 612 may be processed prior to performing objectdetection in block 614. For example, an additional step of resizing,rescaling, and/or resampling the one or more images captured in block612 may be performed prior to block 614. In addition, resizing,rescaling, and/or resampling of the one or more images captured in block612 may be performed in response to determining that a processing timeassociated with using the one or more images originally captured inblock 612 exceeds a predetermined threshold of acceptability.

In determination block 616, the processor may determine whether anarthropod is detected. In response to determining that an arthropod isnot detected (i.e., determination block 616=“No”), the processor mayreturn to block 612 and continue to capture additional images todetermine whether an arthropod is detected in another portion of thescan area. In response to determining that an arthropod is detected(i.e., determination block 616=“Yes”), the processor may initiate thearthropod detected procedure in block 618.

In some embodiments, the arthropod detected procedure of block 618 mayinclude one or more of displaying an indication that a arthropod hasbeen detected, generating an audio indicator corresponding to thedetected arthropod, displaying an image of the detected arthropod,displaying instructions associated with how to remove the arthropod fromthe subject or surface, and displaying medical follow-up information.

FIG. 7 illustrates an exemplary method of performing object detection614 using an electronic arthropod detection device 502 a equipped with athermal imaging capture system according to various embodiments. Withreference to FIGS. 1 and 5, the method 614 may be implemented by one ormore processors of the arthropod detection device 102 and/or theelectronic arthropod detection device 502 a (e.g., processor 516). Inthe method 614 illustrated in FIG. 7, operation blocks 612 and 616 maybe performed as described for like numbered blocks of the method 600illustrated in FIG. 6.

In block 702, the processor may receive one or more infrared (IR) imagescaptured using the thermal image capture system 506. In block 704, theprocessor may scan the one or more IR images to determine whether eachIR image includes a region of interest (ROI). For example, the processorand/or image processor 518 may scan the IR image to determine whether apixel has a contrast value greater than a threshold contrast value.

In determination block 706, the processor may determine whether one ormore ROIs are detected in the IR image. An ROI may be detected inresponse to determining that a plurality of pixels of a region of the IRimage have a contrast value greater than the threshold contrast value.The threshold contrast value may be a single value or a ranges ofvalues.

In some embodiments, determining whether one or more ROIs are detectedin the IR image may include the processor determining whether athreshold number of pixels having a contrast value greater than thethreshold contrast value are present and whether a total number ofpixels corresponding to the contrast value greater than the thresholdcontrast value are arranged within a predetermined area or region. Forexample, the processor may determine that an ROI is detected in the IRimage when a threshold number of pixels having a contrast value greaterthan the threshold contrast value are present in the IR image and thetotal number of pixels corresponding to the contrast value greater thanthe threshold contrast value are arranged in a circular region. Thepredetermined shape or region may correspond to each type of arthropodsuch that the predetermined shape associated with detecting whether atick is present may be different from the predetermined shape associatedwith detecting whether a flea is present. Alternatively, thepredetermined shape or region may be the same for each type of arthropodthat may be detected by the arthropod detection device 102 and/orelectronic arthropod detection device 502 a or 502 b.

In response to determining that an ROI is not detected within the IRimage (i.e., determination block 706=“No”), the processor may return toblock 612 illustrated in FIG. 6 and continue to capture additionalimages to determine whether an arthropod is detected in another portionof the scan area.

In response to determining that an ROI is detected (i.e., determinationblock 706=“Yes”), the processor may define an identification area withinthe IR image in block 708. The defined identification area may be basedon the detected ROI. For example, the processor may define theidentification area to have an area greater than the detected ROI. Theidentification area may be defined to have any shape including a square,a circle, a rectangle, an ellipse, etc. The shape of the definedidentification area may correspond to an approximate shape of thedetected ROI or the shape of the defined identification may have nocorrelation to the shape of the detected ROI. In some embodiments, theprocessor may determine a center point of a detected ROI and define theidentification area such that the center of the defined identificationarea corresponds to the center point of the detected ROI.

In block 710, the processor may send or pass the defined identificationarea of the IR image to an object detection module 520. For example, theprocessor may crop the IR image such that only the definedidentification area is provided to the object detection module 520 toreduce the processing time needed to identify whether an object capturedwithin the defined identification area and/or increase accuracy indetermining whether the object includes identifying characteristics usedby the object detection model.

In some embodiments, the processor may store the entire IR image and/orthe defined identification area of the IR image in the memory 510. Theelectronic arthropod detection device 502 a may use the stored IR imageand/or defined identification area in future scans. Alternatively, theelectronic arthropod detection device 502 a may transmit the IR imageand/or the defined identification area to the server 114 such that theserver 114 may use the IR image and/or the defined identification areato update an object detection model.

After receiving the defined identification area of the IR image, theprocessor implementing the object detection model may perform variousoperations to determine whether one or more objects included within thedefined identification area of the IR image includes identifyingcharacteristics corresponding to the object detection model. In responseto determining whether the defined identification area of the IR imageincludes one or more objects that have characteristics identified withinthe object detection model, the processor implementing the objectdetection model sends the results of the various operations to theprocessor.

In block 712, the processor may receive the results from the objectdetection module and then determine whether an arthropod is detected indetermination block 616 of FIG. 6 based on the results. For example, theresults may indicate whether or not an object within the definedidentification area may be identified as an arthropod. In someembodiments, the results may include an indication of a probability thatthe object within the defined identification area may be an arthropod.

FIG. 8 illustrates another exemplary method of performing objectdetection 612 using an electronic arthropod detection device 502 aequipped with a thermal imaging capture system according to variousembodiments. With reference to FIGS. 1 and 5, the method 612 may beimplemented by one or more processors of the arthropod detection device102 and/or the electronic arthropod detection device 502 a (e.g.,processor 516). In the method 614 illustrated in FIG. 8, operations 612,616, 702, 704, 706, and 712 may be performed as described for likenumbered blocks of method 600 illustrated in FIG. 6 and method 614illustrated in FIG. 7.

In block 802, the processor may receive one or more optical imagescaptured using the optical image capture system 504. While block 802 isillustrated in FIG. 8 as being performed between blocks 702 and 704,block 802 may be performed at any point during method 614. The one ormore optical images may correspond to a substantially similar field ofview of an area of a scan subject that is captured in the IR image. Insome embodiments, one IR image may directly correlate to one opticalimage such that the field of view of the area of the scan subjectcaptured in the IR image is substantially the same as the field of viewof the area of the scan subject captured in the optical image.Alternatively, a plurality of IR images and/or a plurality of opticalimages may be processed to combine a plurality of images such that aresulting IR image and/or a resulting optical image includes a field ofview of the area of the scan subject and the entire area may not havebeen captured in a single image.

In determination block 706, the processor may determine whether an ROIis detected within the IR image. In an exemplary embodiment, the ROI maybe detected when pixels having a contrast value greater than apredetermined contrast value are arranged in a circular shape having adiameter in the range of four to 140 pixels.

In response to determining that an ROI is detected within the IR image(i.e., determination block 706=“Yes”), the processor may identify acenter point of the ROI in the IR image in block 804. For example, theprocessor may first determine an area associated with the edges of thedetected ROI and then determine a center point of the area within theboundaries of the detected ROI.

In block 806, the processor may correlate the center point of the ROIdetected in the IR image with a point on an optical image. The processormay identify and select an optical image that corresponds to the area ofthe scan subject that includes the ROI detected in the IR image. Theprocessor may then identify a point in the optical image that is in asubstantially similar location within the area of the scan subject thatcorrelates to the center point of the ROI detected in the IR image.

In block 808, the processor may define a search area within the opticalimage based the ROI detected in the IR image. The search area may bearranged to cover the center point of the ROI detected in the IR image.In some embodiments, the search area may be centered with respect to thecenter point of the ROI detected in the IR image. The search area of theoptical image may be defined to have a predetermined size and/or shape.In an exemplary embodiment, the search area of the optical image mayhave a square shape such that the length of each side of the squaresearch area is twice the diameter of the ROI detected in the IR image.

In block 810, the processor may scan the search area defined within theoptical image for points of contrast. In some embodiments, the processormay scan a plurality of pixels within the search area to determinewhether a difference between contrast values of adjacent pixels of theoptical image exceeds a predetermined threshold. The predeterminedthreshold may be a single value or a range of values. The processor maydetermine a plurality of points of contrast to identify edges ofcontrast.

In block 812, the processor may define an identification area within theoptical image based on the results of the scan for contrasting edgeswithin the defined search area. The processor may identify a centerpoint of the edges of contrast identified within the defined search areaand define an identification area that overlaps the center point of theedges of contrast. In some embodiments, the area of the identificationarea within the optical image may be substantially similar to the areaof the search area defined within the optical image. Alternatively, thearea of the identification area may be greater than or less than thearea of the search area.

In block 814, the processor may send the identification area definedwithin the optical image to an object detection model. For example, theprocessor may send a portion of the optical image associated with theidentification area defined within the optical image to the objectdetection module 520 where the object detection module 520 may use theobject detection model to determine whether an object included in theidentification area may be an arthropod.

FIG. 9 are images of a scan area for exemplary scan subjects such asanimal subject 902 and human subject 904. With respect to determiningwhether an invertebrate 106 is present on an animal subject 902, image908 may be an IR image of a scan area associated with the animal subject902. Image 908 may be captured using the thermal image capture system506 according to the methods described above. In addition, an exemplaryidentification area defined in block 706 is illustrated in image 908. Asdepicted in image 908, a heat signature of the invertebrate 106 capturedin the IR image is different from the host animal subject 902 such thatthe IR image includes an area of high contrast that corresponds to alocation of the invertebrate 106 on the animal subject. Image 906 may bean optical image of a substantially similar scan area associated withthe animal subject 902. An optical image may or may not be used in adetermination of whether an arthropod is detected on an animal subject.For example, consideration of whether an arthropod is present may bebased solely on one or more IR images. Alternatively, one or moreoptical images may be used before and/or after an ROI is detected withinthe IR image such as in block 706 to determine whether an arthropod ispresent on an animal subject.

With respect to determining whether an invertebrate 106 is present on ahuman subject, image 910 may be captured using the thermal image capturesystem 506. An exemplary ROI detected in block 706 is illustrated inimage 912. Image 912 may be the same image as image 910 or image 912 maybe a different image from image 910. In addition, an exemplary searcharea defined in block 808 and an exemplary identification area definedin block 812 are illustrated in image 914.

FIG. 10 illustrates a method 1000 of updating an object detection modelaccording to various embodiments. With reference to FIGS. 1 and 5, themethod 1000 may be implemented by one or more processors of thearthropod detection device 102, the server 114, and/or the electronicarthropod detection device 502 a or 502 b (e.g., processor 516). Whilemethod 1000 may be illustrated as being performed with respect toupdating an object detection model, method 1000 may alternatively beperformed to generate an original object detection model.

In block 1002, the processor may receive a first image from a firstdevice. The first device may capture the first image and/or the firstdevice may transmit the first image to the processor. In someembodiments, the first device may be the arthropod detection device 102and/or the electronic arthropod detection device 502. The first imagemay be an optical image, an IR image, or a UV image.

In block 1004, the processor may extract metadata from the first image.The metadata may include information associated with one or more of adate the first image was captured, a time the first image was captured,and a geographic location in which the first optical image is captured.In some embodiments, the metadata may include a timestamp (including adate and time associated with when the image was captured) and/or GPScoordinates. The metadata may further include information associatedwith climate, environment, weather, etc.

In block 1006, the processor may identify one or more characteristics ofan object captured in the first image. The processor may use varioustechniques to identify the one or more characteristics including imageprocessing techniques, image recognition techniques, machine learningtechniques, receiving user defined identifications (e.g., from anarthropod specialist or a physician), or a combination thereof. In someembodiments, the one or more characteristics may correspond to a type ofinvertebrate or arthropod such that an object may be identified as anarthropod when the object includes the one or more characteristics.

In block 1008, the processor may classify the first image based on theone or more characteristics of the object included in the first image.For example, if the object includes one or more characteristics that mayallow the object to be determined to be a tick, the first image may beclassified such that the first image may be included when an objectdetection model for a tick is generated.

Optionally, in block 1010, the processor may receive an N^(th) imagefrom an X device. In some embodiments, N may be any whole number and Xmay be any device including the first device. In optional block 1012,the processor may extract metadata from the N^(th) image and in optionalblock 1014, the processor may identify one or more characteristics of anobject included in the N^(th) image. In addition, in optional block1016, the N^(th) image may be classified. Blocks 1010, 1012, 1014, and1016 may be reiterative and may be performed any time the processorreceives a new image.

In block 1018, the processor may identify an object detection model. Forexample, the processor may select an identifier associated with one ormore of a type of arthropod (e.g., tick, bedbug, mite, lice, flea,etc.), a type of scan subject (e.g., human, animal, type of animal(e.g., dog, cat, horse, etc.), and surface), and an image format (e.g.,optical, IR, etc.). The processor may retrieve a previously generatedobject detection model associated with the selected identifier.

In block 1020, the processor may update the identified object detectionmodel. For example, the processor may retrieve images that have beenclassified to correspond with the selected identifier and then updatethe object detection model associated with the selected identifier toinclude the retrieved images that are classified to correspond with theselected identifier.

In optional block 1022, the processor may transmit the updated objectdetection model to one or more devices such as the arthropod detectiondevice 102 and/or the electronic arthropod detection device 502. In someembodiments, the processor may transmit the updated object detectionmodel in response to a request or the processor may transmit the updatedobject detection model at one or more discrete times including at apredetermined interval.

FIGS. 11A and 11B illustrate perspective views of an arthropod detectiondevice 1100 according to various embodiments. FIG. 11A illustrates afront perspective view of the arthropod detection device 1100, and FIG.11B illustrates a back perspective view of the arthropod detectiondevice 1100.

The arthropod detection device 1100 may be a fully integrated device.For example, in some embodiments all hardware components and softwarecomponents of the arthropod detection device 1100 may be provided withina single housing 1114. The arthropod detection device 1100 may include acamera 1102, a thermal (e.g., far infrared) imager 1104, the housing1114, and a display 1116. The arthropod detection device 1100 mayadditionally and/or alternatively include a UV emitting lamp 1106, afirst acoustic sound receiver 1108, a second acoustic sound receiver1110, an automatic mode sensor 1112, an input device 1118, and/or anindicator device 1120. While the camera 1102, the thermal imager 1104,the UV emitting lamp 1106, the first acoustic sound receiver 1108, thesecond acoustic sound receiver 1110, the automatic mode sensor 1112, thedisplay 1116, the input device 1118, and/or the indicator 1120 areillustrated in FIGS. 11A and 11B as having a certain arrangement andconfiguration, such components may be provided within the housing 1114in any location, on any of the surfaces of the housing 1114 to such thatelements have any arrangement and/or configuration.

The camera 1102 may be any camera including a high resolution digitalcamera. In some embodiments, the camera 1102 may include additionallighting capabilities such as LED auto-flash lighting to visualizearthropods. Alternatively, the arthropod detection device 1100 mayfurther include a separate alternative flash or lighting element. Thecamera 1102 may capture still or video images and a user may select animaging mode option for capturing a still image or a continuousrecording mode. In some embodiments, a visual recognition API(application programming interface) may utilize a large stored arthropodimage database to detect the presence of and/or identify one or morearthropods. Characteristic features such as patterning on a tick'sdorsal shield, size, and/or number/positioning of legs may be used byimage recognition software to classify the arthropod.

The thermal imager 1104 may be configured to capture images ofarthropods not readily visual by an unaided eye such as arthropodsobscured by hair, fur, clothing as well as areas with low lighting. Thethermal imager 1104 may be configured to capture still or videothermograms. Thermograms may reveal a color, a size, and/or a shape ofon object included in the recorded image, features of the object whichmay be used with image recognition software to classify the image.Arthropods may have surface temperatures that are distinct from thesurrounding surface and associated with specific spectral colors as wellas characteristic sizes and shapes. Thermography may allow the arthropoddetection device 1100 to detect the presence of arthropods not readilyvisible to the unaided eye/obscured by hair, clothing or otherobstructions. A visual recognition API utilizing a large database ofstored arthropod thermograms (color, size, and shape) and visible lightimages of arthropods may be configured to detect the presence of and/oridentify specific arthropods in areas that may not be visible or audibleto the high-resolution digital camera and/or other imaging modalities.

The UV emitting lamp 1106 may be used to enhance the visibility ofcertain arthropods not readily visible by an unaided eye. Ultravioletlight at selected wavelengths may cause different arthropods tofluoresce or emit visible light with distinct colors. High definitionimages (still and/or video) may then be examined and compared to a largeinternal database of arthropod images under visible and/or ultravioletlighting using a visual recognition API to detect the presence of and/oridentify specific arthropods. A color sensor may also be used to assistin detecting the presence of arthropod fluorescence.

The arthropod detection device 1100 may include the first acoustic soundreceiver 1108 and/or the second acoustic sound receiver 1110. The firstacoustic sound receiver 1108 and the second acoustic sound receiver 1110may be configured to detect active and/or passive sounds generated byone or more arthropods within a scan area. For example, arthropodswithin a scan area may generate incidental sounds such as feeding,chewing, and/or moving noises or communication sounds such as matingsounds and/or warning sounds. Incidental sounds of an arthropod may besofter and harder to detect than communication sounds.

The first acoustic sound receiver 1108 and the second acoustic soundreceiver 1110 may be one or more of a microphone, a transducer, anultrasonic detector, and a piezoelectric sensor. In some embodiments,the first acoustic sound receiver 1108 and/or the second acoustic soundreceiver 1110 may further include an amplifier to amplify an electricsignal generated by the microphone, the transducer, the ultrasonicdetector, and/or the piezoelectric sensor to a level that is sufficientfor detection. In some embodiments, one or both of the first acousticsound receiver 1108 and the second acoustic sound receiver 1110 may beconfigured to also emit ultrasound (e.g., from a transducer orpiezoelectric sensor) and to sense echoes of the ultrasound as may bereturned from arthropods and/or a surface being scanned.

The first acoustic sound receiver 1108 and the second acoustic soundreceiver 1110 may detect acoustic sound waves within the same frequencyrange and/or different frequency ranges. For example, the first acousticsound receiver 1108 and/or the second acoustic sound receiver 1110 maybe configured to detect soundwaves within a human audible range (e.g.,20 Hz to 20 kHz), an ultrasonic range (e.g., 20 kHz to severalgigahertz), and/or a portion thereof. In some embodiments, sinceincidental sounds of an arthropod may be more difficult to detect thancommunication sounds, the first acoustic sound receiver 1108 may betuned to detect incidental sounds of an arthropod and the secondacoustic sound receiver 1110 may be tuned to detect the communicationsounds of the arthropod.

While not illustrated, the arthropod detection device 1100 may furtherinclude a signal processor in communication with the first acousticsound receiver 1108 and the second acoustic sound receiver 1110. Thesignal processor may be configured to minimize and/or filter outundesirable noise or interference detected by the first acoustic soundreceiver 1108 and the second acoustic sound receiver 1110. For example,the signal processor may filter out sound waves outside of apredetermined frequency value. The predetermined frequency value may bea single value or a range of frequency values. In some embodiments, thepredetermined frequency value may be defined for a particular arthropodspecies. Alternatively, the predetermined frequency value may be definedfor different stages of development and/or gender for a particulararthropod species as well as for different species. For example, thepredetermined frequency value associated with a tick larva may bedifferent from an adult tick. Alternatively, the predetermined frequencyvalue of an adult male tick may be different from the predeterminedfrequency value of an adult female tick. Moreover, the predeterminedfrequency value of a bedbug may be different from a mite, etc.

The automatic mode sensor 1112 may be configured to detect ambientlighting and other factors to assist the arthropod detection device 1100in determining the most appropriate imaging modalities for detectingarthropods.

As illustrated in FIG. 11B, the arthropod detection device 1100 mayfurther include a display 1116 configured to display informationassociated with a scan performed by the arthropod detection device 1100,an input device 1118 configured to receive an input (e.g., from a user),and/or an indicator device 1120 configured to provide a visual indicatorof a mode, status, and/or power level of the arthropod detection device1100.

In an exemplary embodiment, a user of the arthropod detection device1100 may survey a region for the presence of arthropods by moving thedetection device or at least an imaging sensor component over thesurface while one or more of the imaging or acoustic modalities isactivated and continuously recording. Alternatively, the user may usethe arthropod detection device 1100 to classify an arthropod by takingone or more still images with one or more of the imaging modalities ordetecting sound with an acoustic modality. In addition, a processorwithin the arthropod detection device 1100 may combine, compare,correlate and/or otherwise use data of two or more of a high-resolutiondigital camera (with or without ultraviolet lighting), an infraredthermal imaging camera, and one or more acoustic sound receivers, inorder to achieve greater sensitivity and/or resolution of arthropodsthan achievable by any one type of sensor alone. In some embodiments,the processor may use a data fusion algorithm that correlates sensordata from two or more different types of sensors in order distinguisharthropods from background signals. For example, data from a thermalimaging camera may be used to identify portions of high-resolutionimages from the digital camera that should be compared to an imagingdatabase. Any of a variety of data fusion algorithms may be implementedby the processor.

The arthropod detection device 1100 may be a standalone deviceconfigured to perform functions limited to arthropod detection or thearthropod detection device 1100 may be an electronic device capablehaving functionalities in addition to arthropod detection such assending/receiving phone calls, email, SMS text, etc., recordingsoundwaves, playing music, capturing images, establishing communicationswith a communication network using one or more communication interfaces,etc. For example, the arthropod detection device 1100 may be asmartphone or other mobile communication device that leverages the oneor more elements of the smartphone or mobile communication device toperform arthropod detection. Specifically, arthropod detection softwaremay be stored in a memory and executed by one or more processors of thesmartphone or mobile communication device such that the capabilities ofthe one or more cameras, microphone, display, speaker, etc. of thesmartphone or mobile communication device are used to captured images,soundwaves, or perform processing according to the various methodsdescribed herein. For instance, the smartphone or mobile communicationdevice may include one or more of the camera 1102, the thermal imager1104, the UV emitting lamp 1106, the first acoustic sound receiver 1108,the second acoustic sound receiver 1110, the automatic mode sensor 1112,the display 1116, the input device 1118, and/or the indicator 1120.

In some embodiments, when a smartphone or mobile communication device isimplemented as the arthropod detection device 1100 an additionalattachment may be coupled to the arthropod detection device 1100 oradditional image and/or sound processing operations may be implementedto enhance the capabilities of the one or more elements of thesmartphone or mobile communication device leveraged for arthropoddetection. For example, a filter, lens (e.g., magnification lens, etc.),IR attachment, UV attachment, or other smartphone or mobilecommunication camera accessory may be removably coupled to one or morecameras of the smartphone or mobile communication device to enhance theimage capture capabilities of the smartphone or mobile communicationdevice. Likewise, a microphone or other soundwave enhancement element(e.g., sonar transmitter, ultrasonic receiver, etc.) may be removablycoupled to the smartphone or mobile communication device to enhance thesoundwave transmission and/or detection capabilities of the smartphoneor mobile communication device.

Additionally or alternatively, additional image and/or signal processingsoftware may be stored and executed by one or more processors of thesmartphone or mobile communication device to enhance images orsoundwaves captured by the smartphone or mobile communication device.For example, additional image processing software may convert one typeof image into another type of image (e.g., IR image to optical image orcolor optical image to black and white optical image, etc.) such that anROI, identification area, and/or search area may be identified in animage generated after processing the captured image (e.g., an image notdirectly captured by a camera of the smartphone or mobile communicationdevice). The smartphone or mobile communication device may alternativelytransmit captured images, soundwaves, etc. via one or more communicationinterfaces to a server such that the server performs the processingand/or analysis.

FIGS. 12 and 13 are views of the arthropod detection device 1100 coupledwith a wand attachment 1200 according to various embodiments. FIG. 12illustrates a front view of the arthropod detection device 1100 coupledwith the wand attachment 1200 in which a telescoping rod 1204 of thewand attachment 1200 is retracted within a handle 1206. FIG. 13illustrates a back view of the arthropod detection device 1100 coupledwith the wand attachment 1200 in which the telescoping rod 1204 of thewand attachment 1200 is in an extended position with respect to thehandle 1206.

The wand attachment 1200 may be permanently coupled to the arthropoddetection device 1100 such that the telescoping rod 1204 is integrallyformed with the arthropod detection device 1100. Alternatively, the wandattachment 1200 may be removably coupled to the arthropod detectiondevice 1100 such that the wand attachment 1200 and the arthropoddetection device 1100 may be selectively separated and the arthropoddetection device 1100 operated without the wand attachment 1200.

The telescoping rod 1204 of the wand attachment 1202 may be configuredto extend and/or retract into handle 1206 for ease in positioning of thearthropod detection device 1100 with respect to a scanning subject. Thetelescoping rod 1204 may allow the arthropod detection device 1100 tobecome more compact when the arthropod detection device 1100 is not inuse. While the wand attachment 1200 is illustrated as including thetelescoping rod 1204, the wand attachment 1200 may include any elementthat extends from the handle 1206 which is configured to allow thearthropod detection device 1100 to be positioned to scan any area of asubject or a surface.

The telescoping rod 1204 may include one or more hinges 1208 and one ormore shafts 1209, 1211. While FIGS. 12 and 13 illustrate two hinges 1208and two shafts 1209, 1211, the telescoping rod 1204 may include anynumber of hinges and any number of shafts 1209, 1211. The hinges 1208may be configured and/or arranged to pivot and bend to provideflexibility such that the telescoping rod 1204 may have up to sixdegrees of freedom of movement for positioning the arthropod detectiondevice 1100 in relation to a human, animal, or surface being scanned inorder to optimize image results. The shafts 1209, 1211 may be configuredto retract within each other such that each shaft may have a differentdiameter. For example, the shaft 1209 closest to the arthropod detectiondevice 1100 may have the smallest diameter and the shaft 1211 closest tothe handle 1206 may have the largest diameter.

As illustrated in FIG. 12, the handle 1206 of the wand assembly 1200 maybe configured to receive the telescoping rod 1204 when the telescopingrod 1204 is in a retracted position. The handle 1206 may be configuredfor right or left hand use. In addition, the handle 1206 may furtherinclude a textured slip-resistant grip 1210 configured for ease andcomfort of grasp as well as the ability to maintain a grasp on thehandle 1206 and a dorsal strap 1212 configured to provide addedprotection against slippage when a scan is being performed by thearthropod detection device 1100.

In some embodiments, the wand assembly 1200 may also include a batterycompartment 1213. The wand assembly 1200 may accommodate disposable orrechargeable batteries.

In addition, as illustrated in FIG. 13, the wand assembly 1200 mayinclude a visual indicator 1214, a port 1216, an input device 1217, anoutput device 1218, and/or a memory card slot 1220. The port 1216 may beconfigured to couple the arthropod detection device 1100 and/or the wandassembly 1200 to another device. For example, the port 1216 may beconfigured to receive a cord to couple the wand assembly 1200 to a powersource (e.g., a wall outlet) or a wired communication interface. In someembodiments, the port 1216 may be a USB port. Alternatively oradditionally, the arthropod detection device 1100 and/or the wandassembly 1200 may also include a wireless communication interface (e.g.,WiFi, Bluetooth, etc.) to wirelessly communicate with other devices. Thewired and/or wireless interface of the wand assembly 1200 may beconfigured to send images or other data to another device such as asmartphone or a computer.

While FIG. 13 illustrates the port 1216, the input device 1217, theoutput device 1218, and/or the memory card slot 1220 arranged on a backsurface of the handle 1206, the port 1216, the input device 1217, theoutput device 1218, and/or the memory card slot 1220 may be provided inany arrangement and/or configuration within the wand attachment 1200.For example, one or more of the port 1216, the input device 1217, theoutput device 1218, and/or the memory card slot 1220 may be included ona front surface of the wand attachment 1200 and/or in the handle 1206.

The input device 1217 of the wand assembly 1200 may be configured toreceive a manual input (e.g., a button pressor touchscreen interactionfrom a user). In some embodiments, in response to receiving an input atthe input device 1217, a sensor and/or scanner of the arthropoddetection device 1100 may be activated or deactivated, a detectionmodality and/or a scan mode of the arthropod detection device 1100 maybe selected, and/or power to the arthropod detection device 1100 and/orthe wand assembly 1200 may be turned on/off. Alternatively, the wandassembly 1200 may automatically turn the arthropod detection device 1100and/or the wand assembly 1200 on or off when a sensor and/or a scannerof the arthropod detection device 1100 is activated or deactivated.

The display 1116 may be configured to display captured or scanned imagesand/or information associated with a scan performed by the arthropoddetection device 1100. The display 1116 may be any type of display suchas an LED display, an LCD display, an OLED display, an AMOLED display,etc. or a combination thereof. The housing 1114 may include raised,impact resistant edges 1220 configured to protect detection componentsof the arthropod detection device 1100 such as the camera 1102, thethermal imager 1104, the UV emitting lamp 1106, the first acoustic soundreceiver 1108, the second acoustic sound receiver 1110, the automaticmode sensor 1112, the display 1116, the input device 1118, and/or theindicator 1120.

The input device 1217 may be configured to allow a user to select aspecific imaging modality or multiple modalities. In some embodiments,in response to receiving an input associated with an automatic mode atthe input device 1217, the arthropod detection device 1100 mayautomatically select one or more imaging modalities while scanning forthe arthropod. The output device 1218 may be an auditory alert speakerconfigured to notify a user that an arthropod has been detected. Thevisual indicator 1214 may be configured to provide a visual indicationof information associated with the arthropod detection device 1100and/or the wand assembly 1200. For example, the visual indicator 1214may be an LED element configured to notify a user that an arthropod hasbeen detected and/or identified, a status and/or mode of the arthropoddetection device 1100, and/or a power level of the arthropod detectiondevice 1100 and/or the wand assembly 1200.

FIGS. 14-16 are views of another arthropod detection device 1400including a processing device 1402 and an arthropod detection module1406 according to various embodiments. FIGS. 14A, 14B, 14C, and 14Dillustrates the individual components of the arthropod detection device1400 when the arthropod detection device 1400 is disassembled. FIG. 15illustrates a front view of the arthropod detection device 1400 when theseparate components are assembled. FIG. 16 illustrates a back view ofthe arthropod detection device 1400 when the separate components areassembled.

As illustrated in FIGS. 14A, 14B, 14C, and 14D, 15, and 16, thearthropod detection device 1400 may include a processing device 1402,side clamps 1404, an arthropod detection module 1406, and a wandattachment 1406.

The processing device 1402 may be a wireless communication device suchas a smartphone. The processing device 1402 may include a display 1401and a speaker 1403. While not illustrated, the processing device 1402may further include a microprocessor, application software includingimage and/or sound recognition software, image storage, memory, a modeselection application, and a communication interface for wirelesscommunications.

In some embodiments, software and/or an application configured todetermine whether an arthropod is detected may be stored in memory ofthe processing device 1402. The software and/or application may processinformation received from the arthropod detection module 1406 todetermine whether an arthropod is detected within an area scanned by thearthropod detection device 1400. As illustrated in FIG. 15, an icon 1502associated with the software and/or application may be displayed on thedisplay 1401 of the processing device 1402. In response to detecting aninput associated with the icon 502, the processing device 1402 mayexecute the software and/or application stored in the processing device1402.

The arthropod detection device 1400 may further include side clamps1404. The side clamps 1404 may include a first side clamp and a secondside claim configured to secure the arthropod detection module 1406 tothe right and left sides of the processing device 1402. While the sideclamps 1404 are illustrated as having a particular size, shape, andconfiguration, the first side clamp and/or the second side clamp mayhave any shape, size, and/or configuration that allows the side clamps1404 to secure and/or prevent the processing device 1402, the arthropoddetection module 1406, and/or the wand attachment 1408 from prematurelyseparating after being assembled.

The arthropod detection device 1400 may include an arthropod detectionmodule 1406. For clarity and ease of explanation, the arthropoddetection module 1406 may include one or more of the like numberedcomponents previously described with reference to FIGS. 11A and 13.Specifically, the arthropod detection module 1406 may include a camera1102, and a thermal imager 1104, and in some embodiments, a UV emittinglamp 1106, a first acoustic sound receiver 1108, a second acoustic soundreceiver 1110, an automatic mode sensor 1112, a memory card slot 1220, avisual indicator 1214, and/or a port 1216.

In addition, the arthropod detection module 1406 may further include aconnector 1407 configured to allow the arthropod detection module 1406to communicate with the processing device 1402. In some embodiments, theconnector 1407 may be configured to be coupled with a port of theprocessing device 1402. For example, the connector 1407 may be insertedinto a port of the processing device 1402 that is used to recharge theprocessing device 1402 or a port configured to receive an audio plugsuch as a headphone port. In another embodiment, the connector 1407 maybe a USB connector.

The arthropod detection device 1400 may also include a wand assemblyattachment 1408. For clarity and ease of explanation, the wand assemblyattachment 1408 may include one or more of the like numbered componentspreviously described with reference to FIGS. 12 and 13. Specifically,the wand assembly attachment 1408 may include a telescoping rod 1204, ahandle 1206, a hinge 1208, a textured slip-resistant grip 1210, and adorsal strap 1212.

In some embodiments, the arthropod detection module 1406 may furtherinclude a memory and/or one or more processors configured to perform oneor more operations of the arthropod detection method. For example, thearthropod detection module 1406 may capture one or more images and/orsoundwaves and transmit the captured images and/or soundwaves to theprocessing device 1402 such that the processing device 1402 performsobject detection (e.g., block 614) using the captured images and/orsoundwaves received from the arthropod detection module 1406.Alternatively, the arthropod detection module 1406 may perform all ofthe processing operations such that the arthropod detection module 1406determines whether an arthropod has been detected (e.g., decision block616) and sends an indication to the processing device 1402 to initiatethe arthropod detected procedure (e.g., block 618).

In addition, the arthropod detection module 1406 may selectively performone or more operations of the arthropod detection method. For example,the arthropod detection module 1406 may detect an ROI (e.g., decisionblock 706) and then capture additional images or send previouslycaptured images to the processing device 1402 to define anidentification area and/or a scan area. Alternatively, the arthropoddetection module 1406 may detect an ROI and define an identificationarea and then send the results of defining the identification area tothe processing device 1402.

In some embodiments, the processing device 1402 may receive information(e.g., software updates, firmware updates, an object detection model,etc.) via a communication interface of the processing device 1402 andthen transfer the received information to the arthropod detection module1406 upon being coupled via the connector 1407. Alternatively, thearthropod detection module 1406 may further include one or morecommunication interfaces to transmit and/or receive information from anetwork, the processing device 1402, or another device.

The wand assembly attachment 1408 may also include a coupling element1428 configured to secure the telescoping rod 1204 to the arthropoddetection module 1406. The coupling element 1428 may include any type ofconnector that allows the coupling element 1428 to be removably coupledwith the arthropod detection module 1406. For example, the couplingelement 1428 may be a twist and lock element, a latch, etc.

In some embodiments, the separate components of the arthropod detectiondevice 1400 may be manually assembled by a user such that the arthropoddetection device 1400 may be used to scan for and/or alert a user whenarthropods are detected.

The arthropod detection devices 1100 and/or 1400 may be configured forthe purpose of detecting one or more arthropods on a human, animaland/or surface, and then alerting the user if and when one or morearthropods are detected. In some embodiments, the arthropod detectiondevices 1100 and/or 1400 may perform similarly to a metal detector inthat a user may scan a subject using the arthropod detection devices1100 and/or 1400 at a first scan rate and in response to detecting anobject that may be an arthropod, the devices 1100 and/or 1400 may emitan auditory alert. When a greater number of auditory alerts aregenerated, the user may scan a subject using the devices 1100 and/or1400 at a second scan rate slower than the first scan rate. During thescans performed at the second scan rate, the devices 1100 and/or 1400may capture additional images such as optional images received in block802 illustrated in FIG. 8. In some embodiments, when not in use, devices1100 and/or 1400 may be carried and stored in a full protective casedesigned to resist effects of impact and climate.

The various embodiments (including, but not limited to, embodimentsdiscussed above with reference to FIGS. 1-16) may be implemented in anyof a variety of personal devices (i.e. arthropod detection device 102,electronic arthropod detection device 502, arthropod detection devices1100 and/or 1400), an example of which is illustrated in FIG. 17. Forexample, the personal device 1700 may include a processor 1701 coupledto a touch screen controller 1704 and an internal memory 1702. Theprocessor 1701 may be one or more multicore integrated circuits (ICs)designated for general or specific processing tasks. The internal memory1702 may be volatile or non-volatile memory, and may also be secureand/or encrypted memory, or unsecure and/or unencrypted memory, or anycombination thereof. The touch screen controller 1704 and the processor1701 may also be coupled to a touch screen panel 1712, such as aresistive-sensing touch screen, capacitive-sensing touch screen,infrared sensing touch screen, etc.

In some embodiments, personal device 1700 may include one or more radiosignal transceivers 1708 (e.g., Peanut®, Bluetooth®, Zigbee®, Wi-Fi,cellular, etc.) and antennae 1710, for sending and receiving, coupled toeach other and/or to the processor 1701. The transceivers 1708 andantennae 1710 may be used with the above-mentioned circuitry toimplement the various wireless transmission protocol stacks andinterfaces. The personal device 1700 may include a cellular networkwireless modem chip 1716 that enables communication via a cellularnetwork and is coupled to the processor.

The personal device 1700 may include a peripheral device connectioninterface 1718 coupled to the processor 1701. The peripheral deviceconnection interface 1718 may be singularly configured to accept onetype of connection, or multiply configured to accept various types ofphysical and communication connections, common or proprietary, such asUSB, FireWire, Thunderbolt, or PCIe. The peripheral device connectioninterface 1718 may also be coupled to a similarly configured peripheraldevice connection port (not shown).

The personal device 1700 may also include speakers 1714 for providingaudio outputs. The personal device 1700 may also include a housing 1720,constructed of a plastic, metal, or a combination of materials, forcontaining all or some of the components discussed herein. The personaldevice 1700 may include a power source 1722 coupled to the processor1701, such as a disposable or rechargeable battery. The rechargeablebattery may also be coupled to the peripheral device connection port toreceive a charging current from a source external to the personal device1700.

The personal device 1700 may also include a secure area and/or a trustedexecution environment. The trusted execution environment may include oneor more processors and/or memory to perform secure operations that aremasked from the rest of the elements of the personal device 1700. Forexample, the trusted execution environment may include a digital rightsmanagement (DRM) client or agent such as a content decryption module(CDM) in order to perform operations in a secure environment to reducethe risk of undesired interception of secure data.

Various embodiments (including, but not limited to, embodimentsdescribed with reference to FIGS. 1 and 10) may also be implemented onany of a variety of server devices, an example of which (e.g., server114) is illustrated in FIG. 18. With reference to FIGS. 1 and 10, theserver device 1800 typically includes a processor 1801 coupled tovolatile memory 1802, and may also include and a large capacitynonvolatile memory, such as a disk drive 1804. The server device 1800may also include a floppy disc drive, compact disc (CD) or DVD discdrive 1806 coupled to the processor 1801. The server device 1800 mayalso include network communication ports 1803 coupled to the processor1801 for, among other things, establishing network interface connections1807 with a communication network (such as a local area network coupledto other broadcast system computers and servers, a wide area network, acontent data network, the public switched telephone network, and/or acellular data network (e.g., CDMA, TDMA, GSM, PCS, 3G, 4G, LTE, or anyother type of cellular data network). The server device 1800 may alsoinclude output ports 1808 for providing content to wirelesscommunication devices, and/or providing content to an output device,such as a display and/or a speaker.

The processors 1701 and 1801 may be any programmable microprocessor,microcomputer or multiple processor chip or chips that can be configuredby software instructions (applications) to perform a variety offunctions, including the functions of the various embodiments describedabove. In some devices, multiple processors may be provided, such as oneprocessor dedicated to wireless communication functions and oneprocessor dedicated to running other applications. Typically, softwareapplications may be stored in the internal memory before they areaccessed and loaded into the processors 1701 and 1801. The processors1701 and 1801 may include internal memory sufficient to store theapplication software instructions. In many devices, the internal memorymay be a volatile or nonvolatile memory, such as flash memory, or amixture of both. For the purposes of this description, a generalreference to memory refers to memory accessible by the processors 1701and 1801 including internal memory or removable memory plugged into thedevice and memory within the processors 1701 and 1801 themselves.

FIG. 19 is a component block diagram illustrating components that may beincluded within an electronic device configured to implement variousconfigurations of the systems and methods of determining whether anarthropod is present according to various embodiments. Examples of theelectronic device 1900 include a camera, a video camcorder, a digitalcamera, a cellular phone, a smartphone, a computer (e.g., a desktopcomputer, a laptop computer, etc.), a tablet device, a mobilecommunication device, a drone, an unmanned aerial vehicle (UAV), etc.One or more of the components or elements of the electronic device 1900may be implemented in hardware (e.g., circuitry) or a combination ofhardware and software (e.g., at least one processor with instructions).The electronic device 1900 may be implemented in accordance with thearthropod detection device 102, the electronic arthropod detectiondevice 502, and/or the arthropod detection devices 1100, 1400. Theelectronic device 1900 may include a processor 1924, which may be ageneral purpose single-chip or multi-chip microprocessor (e.g., an ARM)or a special purpose microprocessor such as digital signal processor(DSP).

The electronic device 1900 may also include memory 1902 coupled to theprocessor 1924. The memory 1902 may be any electronic component capableof storing electronic information. The memory 1902 may be embodied asrandom access memory (RAM), read-only memory (ROM), magnetic diskstorage medial, optical storage media, flash memory devices in RAM,on-board memory included with the processor, EPROM memory, EEPROMmemory, registers, and so forth including combinations thereof.

Data 1906 and instructions 1904 may be stored in the memory 1902. Theinstructions 1904 may be executable by the processor 1924 to implementone or more of the methods, procedures, operations, and/or functionsdescribed herein. Executing the instructions 1904 may involve the use ofthe data 1906 stored in the memory. When the processor 1924 executes theinstructions 1904, various portions of the instructions 1926 may beloaded onto the processor 1924 and/or various pieces of data 1928 may beloaded onto the processor 1924.

The electronic device 1900 may also include a transmitter 1910 and areceiver 1914 to allow transmission and reception of signals to and fromthe electronic device 1900. The transmitter 1910 and the receiver 1914may be collectively referred to as transceiver 1908. One or moreantennas 1912, 1916 may be electrically coupled to the transceiver 1908.The electronic device 1900 may also include (not shown) multipletransmitters, multiple receivers, multiple transceivers and/oradditional antennas.

The electronic device 1900 may also include a communication interface1920. The communication interface 1920 may allow and/or enable one ormore kinds of input and/or output. For example, the communicationinterface 1920 may include one or more ports and/or communicationdevices for linking other devices to the electronic device 1900. In someconfigurations, the communication interface 1920 may include thetransmitter 1910, the receiver 1914, or both (e.g., transceiver 1908).Additional or alternatively, the communication interface 1920 mayinclude one or more other interfaces (e.g., touchscreen, keypad,keyboard, microphone, camera, etc.). The communication interface 1920may enable a user to interact with the electronic device 1900.

In addition, the electronic device 1900 may further include a display1930 and one or more camera(s) 1932. The one or more camera(s) 1932 mayinclude optical components configured to capture one or more images. Insome embodiments, camera(s) 1932 of the electronic device 1900 mayinclude two or more image sensors configured to capture images atdifferent wavelengths. For example, the camera(s) 1932 may be configuredto capture visible light images, IR images, and/or UV images. Thedisplay 1930 may be configured to display information includinginformation associated with one or more images captured by the camera(s)1932.

The electronic device 1900 may also include one or more sensor(s) 1922.The one or more other sensor(s) 1922 may be any type of sensor includinga proximity sensor, an ambient light sensor, an accelerometer, a nearfield communication sensor, a gyroscope, a magnetometer, a temperaturesensor, a barometric pressure, a color sensor, an ultraviolet sensor, aGPS sensor, etc.

The various components of the electronic device 1900 may be coupledtogether by one or more buses, which may include a power bus, a controlsignal bus, a status signal bus, a data bus, etc. For the sake ofclarity, the various buses are illustrated in FIG. 19 as a bus system1918.

FIG. 20 illustrates a method of performing object detection 612 using anelectronic arthropod detection device 502 b equipped with a mm-wave orTHz radar or a far infrared sensor according to some embodiments. Withreference to FIGS. 1 and 5B, the method 2000 may be implemented by oneor more processors of the arthropod detection device 102 and/or theelectronic arthropod detection device 502 a (e.g., processor 516).

In block 2002, the processor may receive data from a mm-wave or THzradar or far infrared sensor 509 while a user scans a surface or asubject. Thus, in block 2002, the processor receives data from a sensorsensitive to a first frequency band of electromagnetic radiation that iswithin at least one of a millimeter wave band, a terahertz band, or afar infrared band. In some embodiments, the data may be in the form ofreceived signals or detection indications that indicate whetherradiation has been received consistent with a reflection off of anarthropod. In some embodiments, the data may be in the form of an arrayof detection signals generated by an array of receivers configured togenerate an image of reflected signals. In some embodiments, the mm-waveor THz radar or far infrared sensor 509 may be configured to process thereceived radiation signals to generate process data or an image, that isprovided to the processor in block 2002.

In block 2004, the processor may process the sensor data to determinewhether an arthropod is present within the field of view of the mm-waveor THz radar or far infrared sensor 509. For example, the processor maydetermine whether a received radiation signal is consistent with areflection from an arthropod. As another example, the processor mayanalyze an image of reflected signals provided by the mm-wave or THzradar or far infrared sensor 509 to determine whether any spotsconsistent with an arthropod are detected.

In determination block 2006, the processor may determine whether anarthropod has been detected (or the likelihood that an arthropod ispresent exceeds a predefined threshold) based upon the analysisperformed in block 2004.

In response to determining that an arthropod is not detected within thedata provided by the mm-wave or THz radar or a far infrared sensor(i.e., determination block 2006=“No”), the processor may return tocontinue to receive data from the radar or sensor in block 2002 andprocess the sensor data in block 2004.

In response to deter mining that an arthropod is detected within thedata provided by the mm-wave or THz radar or a far infrared sensor(i.e., determination block 2006=“Yes”), the processor may signal to anoperator that an arthropod is detected in block 2008 so that the usercan perform a more detailed inspection using a camera (e.g., a highdefinition visible light camera and/or an IR camera) according to themethod 600 described with reference to FIG. 6 (e.g., beginning in block612). Due to the relatively long wavelength to which the mm-wave or THzradar or a far infrared sensor is sensitive, recognition andclassification of an arthropod may not be possible. Therefore, when thepresence (or likely presence) of an arthropod is detected by analysis ofdata from the mm-wave or THz radar or a far infrared sensor, theprocessor may generate an audible, haptic and/or visible alert informingthe user that a closer inspection using a camera sensitive to a secondfrequency of electromagnetic radiation having a shorter wavelength(i.e., visible or IR light) may be appropriate. In response, the usermay more closely scan the area using an imaging sensor sensitive tovisible and/or IR light, as well as perform other actions to facilitatethe imaging scan, such as combing through the hair or fur within thefield of view of the higher resolution camera.

FIG. 21 illustrates another exemplary method 2100 of performingarthropod detection using an electronic arthropod detection device 502 aequipped with a thermal imaging capture system according to variousembodiments. With reference to FIGS. 1, 5 and 20, the method 2100 may beimplemented by one or more processors of the arthropod detection device102 and/or the electronic arthropod detection device 502 b (e.g.,processor 516).

In block 2002, the processor may receive data from a mm-wave or THzradar or far infrared sensor 509 as described in the method 2000 withreference to FIG. 20 while a user scans a surface or a subject. Thus, inblock 2002, the processor receives data from a sensor sensitive to afirst frequency band of electromagnetic radiation that is within atleast one of a millimeter wave band, a terahertz band, or a far infraredband.

In block 2102, the processor may receive one or more optical imagescaptured using an image capture system 504 that is sensitive to a secondfrequency band, such as a visible light band (e.g., a high definitionvisible light camera), an IR band (e.g., an IR camera), or both a highdefinition visible light camera and an IR camera. While block 2102 isillustrated in FIG. 21 as being performed between blocks 2002 and 2004,block 2102 may be performed at any point during the method 2100.

In block 2104, the processor may process sensor data received from themm-wave or THz radar or far infrared sensor received in block 2002 incombination with the visible light and/or IR images received in block2102 to determine whether an arthropod is present or likely present(e.g., a determine probability of presence exceeds a predeterminedthreshold). By combining information from lower but deeper penetratingmm-wave or THz radar or far infrared sensor with the high-resolutioninformation obtained from visible and/or IR images, processor may beable to detect the presence of an arthropod with greater reliabilitythan achievable using just one of the types of sensors. For example, inconditions in which there is no fur present, and arthropod is likely tobe directly contacting the skin of the individual such that there may belittle contrast observable by a mm-wave or THz radar or far infraredsensor, but a visible and/or IR image may be able to directly image thearthropod. As another example, in conditions in which an arthropod maybe hidden within fur, a visible and/or IR image may be unable to imagethe arthropod directly, however the arthropod may be suspended above theskin of the individual such that a mm-wave or THz radar or far infraredsensor is able to resolve the arthropod. Thus, by scanning and imaging asurface or subject using two different frequency bands, variousembodiments can improve the likelihood that an arthropod will bedetected and image with sufficient clarity to enable recognition.

In determination block 2106, the processor may determine whether anarthropod is present based upon the analysis performed in block 2104. Solong as no arthropod is detected (i.e., determination block 2106=“No”),the processor may continue to receive data from the mm-wave or THz radaror far infrared sensor in block 2002, receive image data in block 2102,and process the sensor and optical image data in block 2104.

In response to determining that an arthropod is present (i.e.,determination block 2106=“Yes”), the processor may define a search areafor the visible light and/or IR image(s) in block 2108. For example, theprocessor may use the sensor and image data to determine a portion ofthe image data that should be processed in more detail to identify thepresence and type of arthropod. This operation may involve correlatingor registering the sensor and image data. The type of processinginvolved in block 2108 may depend upon the nature of the area scanned inblocks 2002 and 2102 (e.g., whether there is substantial fur or not).

In block 810, the processor may process the search area defined withinthe optical image or images for points of contrast as described in themethod 800 for the like numbered block with reference to FIG. 8. In someembodiments, the processor may evaluate a plurality of pixels within thesearch area of the image data to determine whether a difference betweencontrast values of adjacent pixels of the optical image exceeds apredetermined threshold. The predetermined threshold may be a singlevalue or a range of values. The processor may determine a plurality ofpoints of contrast to identify edges of contrast.

In block 812, the processor may define an identification area within theoptical image based on the results of the image processing forcontrasting edges within the defined search area as described in themethod 800 for the like numbered block with reference to FIG. 8. Theprocessor may identify a center point of the edges of contrastidentified within the defined search area and define an identificationarea that overlaps the center point of the edges of contrast. In someembodiments, the area of the identification area within the opticalimage may be substantially similar to the area of the search areadefined within the optical image. Alternatively, the area of theidentification area may be greater than or less than the area of thesearch area.

In block 814, the processor may send the identification area definedwithin the optical image to an object detection model as described inthe method 800 for the like numbered block with reference to FIG. 8. Forexample, the processor may send a portion of the optical imageassociated with the identification area defined within the optical imageto the object detection module 520 where the object detection module 520may use the object detection model to determine whether an objectincluded in the identification area may be an arthropod.

In block 712, the processor may receive the results from the objectdetection module and determine based on the results whether an arthropodis detected in determination block 616 as described in the method 600for the like numbered block with reference to FIG. 6. For example, theresults may indicate whether or not an object within the definedidentification area may be identified as an arthropod. In someembodiments, the results may include an indication of a probability thatthe object within the defined identification area may be an arthropod.

In response to determining that an arthropod is detected (i.e.,determination block 616=“Yes”), the processor may initiate the arthropoddetected procedure in block 618 as described in the method 600 for thelike numbered block with reference to FIG. 6.

In response to determining that an arthropod is detected (i.e.,determination block 616=“Yes”), the processor may continue to receivedata from the mm-wave or THz radar or far infrared sensor in block 2002,receive image data in block 2102, and process the sensor and opticalimage data in block 2104 as described above.

FIG. 22 is a component block diagram illustrating components that may beincluded within an electronic device including a mm-wave or THz radar ora far infrared sensor configured to implement various configurations ofthe systems and methods of determining whether an arthropod is presentaccording to various embodiments. Examples of the electronic device 2200include a camera, a video camcorder, a digital camera, a cellular phone,a smartphone, a computer (e.g., a desktop computer, a laptop computer,etc.), a tablet device, a mobile communication device, a drone, anunmanned aerial vehicle (UAV), etc. One or more of the components orelements of the electronic device 2200 may be implemented in hardware(e.g., circuitry) or a combination of hardware and software (e.g., atleast one processor with instructions). The electronic device 2200 maybe implemented in accordance with the arthropod detection device 102,the electronic arthropod detection device 502 b, and/or the arthropoddetection devices 1100, 1400. The electronic device 2200 may include aprocessor 1924, which may be a general purpose single-chip or multi-chipmicroprocessor (e.g., an ARM) or a special purpose microprocessor suchas digital signal processor (DSP).

The electronic device 2200 may include the components 1902-1932 of theelectronic device 1900 described with reference to FIG. 19. Inparticular, the electronic device 2200 may include a camera 1932configured to obtain images in visible light and/or IR light. In someembodiments, the electronic device 2200 may include a visible lightcamera and an IR camera (generally indicated as 1932). Additionally, theelectronic device 2200 may include a mm-wave or THz radar or a farinfrared sensor 2202 configured to output detection and/or image data tothe processor(s) 1924 (e.g., via a bus 1918).

The mm-wave or THz radar or a far infrared sensor 2202 may include atleast one emitter 2204 configured to emit electromagnetic radiation witha wavelength sufficiently long enough to penetrate human and animal hairfollicles but short enough to reflect off or be absorbed by an arthropodof interest (e.g., a deer tick nymph). For example, the at least oneemitter 2204 may be configured to emit terahertz radiation at 300 GHz orhigher frequencies.

The mm-wave or THz radar or a far infrared sensor 2202 may include atleast one receiver 2206 configured to receive electromagnetic radiationwith a wavelength sufficiently long enough to penetrate human and animalhair follicles but short enough to reflect off or be absorbed by anarthropod of interest (e.g., a deer tick nymph). In embodiments in whichthe mm-wave or THz radar or a far infrared sensor 2202 includes at leastone emitter 2204, the receiver 2206 configured to receiveelectromagnetic radiation with the same (or including) wavelength(s) asemitted by the at least one emitter 2204. In some embodiments, thereceiver 2206 may be an array of receiver antennas configured to enableobtaining an image of signals.

Data 1906 and instructions 1904 may be stored in the memory 1902. Theinstructions 1904 may be executable by the processor 1924 to implementone or more of the methods, procedures, operations, and/or functionsdescribed herein. Executing the instructions 1904 may involve the use ofthe data 1906 stored in the memory. When the processor 1924 executes theinstructions 1904, various portions of the instructions 1926 may beloaded onto the processor 1924 and/or various pieces of data 1928 may beloaded onto the processor 1924.

The foregoing method descriptions and the process flow diagrams areprovided merely as illustrative examples and are not intended to requireor imply that the operations of the various embodiments must beperformed in the order presented. As will be appreciated by one of skillin the art the order of operations in the foregoing embodiments may beperformed in any order. Words such as “thereafter,” “then,” “next,” etc.are not intended to limit the order of the operations; these words aresimply used to guide the reader through the description of the methods.Further, any reference to claim elements in the singular, for example,using the articles “a,” “an” or “the” is not to be construed as limitingthe element to the singular.

The various illustrative logical blocks, modules, circuits, andalgorithm operations described in connection with the embodimentsdisclosed herein may be implemented as electronic hardware, computersoftware, or combinations of both. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and operations have beendescribed above generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present invention.

The hardware used to implement the various illustrative logics, logicalblocks, modules, and circuits described in connection with the aspectsdisclosed herein may be implemented or performed with a general purposeprocessor, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic,discrete hardware components, or any combination thereof designed toperform the functions described herein. A general-purpose processor maybe a microprocessor, but, in the alternative, the processor may be anyconventional processor, controller, microcontroller, or state machine. Aprocessor may also be implemented as a combination of computing devices,e.g., a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. Alternatively, some operations ormethods may be performed by circuitry that is specific to a givenfunction.

In one or more exemplary aspects, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable medium ornon-transitory processor-readable medium. The operations of a method oralgorithm disclosed herein may be embodied in a processor-executablesoftware module and/or processor-executable instructions, which mayreside on a non-transitory computer-readable or non-transitoryprocessor-readable storage medium. Non-transitory server-readable,computer-readable or processor-readable storage media may be any storagemedia that may be accessed by a computer or a processor. By way ofexample but not limitation, such non-transitory server-readable,computer-readable or processor-readable media may include RAM, ROM,EEPROM, FLASH memory, CD-ROM or other optical disk storage, magneticdisk storage or other magnetic storage devices, or any other medium thatmay be used to store desired program code in the form of instructions ordata structures and that may be accessed by a computer. Disk and disc,as used herein, includes compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and Blu-ray disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers. Combinations of the above are also includedwithin the scope of non-transitory server-readable, computer-readableand processor-readable media. Additionally, the operations of a methodor algorithm may reside as one or any combination or set of codes and/orinstructions on a non-transitory server-readable, processor-readablemedium and/or computer-readable medium, which may be incorporated into acomputer program product.

The preceding description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the claims. Variousmodifications to these embodiments will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other embodiments without departing from the scope of theclaims. Thus, the present disclosure is not intended to be limited tothe embodiments shown herein but is to be accorded the widest scopeconsistent with the following claims and the principles and novelfeatures disclosed herein.

What is claimed is:
 1. A method of arthropod detection using anelectronic arthropod detection device, the method comprising: receivingan indication associated with a type of arthropod to be detected;determining by the electronic arthropod detection device, a type of scanto perform based on the received indication associated with the type ofarthropod to be detected; retrieving, from a memory of the electronicarthropod detection device, an object detection model from a pluralityof object detection models based on the determined type of scan toperform; scanning a subject using at least one of a mm-wave, terahertz,or far infrared sensor of the electronic arthropod detection device thatis sensitive to wavelengths longer than 0.10 mm; determining, by aprocessor of the electronic arthropod detection device, whether anarthropod is or is likely present in a region of interest (ROI) based onthe scan of the subject using the at least one of a mm-wave, terahertz,or far infrared sensor, and initiating an arthropod detected procedureusing the retrieved object detection model in response to determiningthat an arthropod is or is likely present in the ROI.
 2. The method ofclaim 1, wherein initiating an arthropod detected procedure using theretrieved object detection model comprises: capturing, by the electronicarthropod detection device, at least one image of the ROI by an imagingsensor sensitive to a visible band of electromagnetic radiation; andusing the retrieved object detection model to determine whether anarthropod is detected in the at least one image captured by the imagingsensor sensitive to a visible band of electromagnetic radiation.
 3. Themethod of claim 1, wherein initiating the arthropod detected procedureusing the retrieved object detection model comprises one or more ofdisplaying an indication that an arthropod has been detected, generatingan audio indicator corresponding to the detected arthropod, displayingan image of the detected arthropod, displaying instructions associatedwith how to remove the arthropod from the subject or the surface, anddisplaying medical follow-up information.
 4. The method of claim 1,wherein the plurality of object detection models are derived at least inpart using crowd sourcing solutions.
 5. An electronic arthropoddetection device, comprising: at least one of a mm-wave, terahertz, orfar infrared sensor sensitive to wavelengths longer than 0.10 mm; acamera configured to generate an image from a visible light band ofelectromagnetic radiation; a memory; and a processor coupled to thememory, the camera and the at least one of a mm-wave, terahertz, or farinfrared sensor, wherein the processor is configured withprocessor-executable instructions to perform operations comprising:receiving an indication associated with a type of arthropod tobe-detected; determining a type of scan to perform based on the receivedindication associated with the type of arthropod to be detected;retrieving an object detection model from a plurality of objectdetection models stored in the memory based on the determined type ofscan to perform; receiving data from the at least one of a mm-wave,terahertz, or far infrared sensor sensitive to wavelengths longer than0.10 mm while the electronic arthropod detection device scans a subject;determining whether an arthropod is or is likely present in a region ofinterest (ROI) based on the received data from the at least one of amm-wave, terahertz, or far infrared sensor sensitive to wavelengthslonger than 0.10 mm, and initiating an arthropod detected procedureusing the retrieved object detection model in response to determiningthat an arthropod is or is likely present in the ROI.
 6. The electronicarthropod detection device of claim 5, wherein the processor isconfigured with processor-executable instructions to perform operationssuch that initiating the arthropod detected procedure using theretrieved object detection model comprises one or more of displaying anindication that an arthropod has been detected, generating an audioindicator corresponding to the detected arthropod, displaying an imageof the detected arthropod, displaying instructions associated with howto remove the arthropod from the subject or the surface, or displayingmedical follow-up information.
 7. The electronic arthropod detectiondevice of claim 5, wherein the processor is configured withprocessor-executable instructions to perform operations such thatinitiating the arthropod detected procedure using the retrieved objectdetection model comprises: capturing, by the camera, at least one imageof the ROI in response to determining that an arthropod is or is likelypresent in the ROI; and processing the at least one image using theretrieved object detection model to determine whether an arthropod isdetected in the at least one image.
 8. The electronic arthropoddetection device of claim 5, wherein the processor is configured withprocessor-executable instructions to perform operations such that theplurality of object detection models are derived at least in part usingcrowd sourcing solutions.
 9. An arthropod detection device, comprising:means receiving an indication associated with a type of arthropod to bedetected; means for determining a type of scan to perform based on thereceived indication associated with the type of arthropod to bedetected; means for retrieving an object detection model, from aplurality of object detection models stored in a means forelectronically storing the plurality of object detection models, basedon the determined type of scan to perform; means for scanning a subjectusing at least one of a mm-wave, terahertz, or far infrared sensor ofthe electronic arthropod detection device that is sensitive towavelengths longer than 0.10 mm; means for determining, by a processorof the electronic arthropod detection device, whether an arthropod is oris likely present in a region of interest (ROI) based on a scan of thesubject using the at least one of a mm-wave, terahertz, or far infraredsensor, and means for initiating an arthropod detected procedure usingthe retrieved object detection model in response to determining that anarthropod is or is likely present in the ROI.
 10. The arthropoddetection device of claim 9, wherein means for initiating the arthropoddetected procedure using the retrieved object detection model comprises:means for capturing at least one image of the ROI by an imaging sensorsensitive to a visible band of electromagnetic radiation; and means forprocessing the at least one image using the retrieved object detectionmodel to determine whether an arthropod is detected in the at least oneimage.
 11. The arthropod detection device of claim 9, wherein means forinitiating the arthropod detected procedure using the retrieved objectdetection model comprises one or more of means for displaying anindication that an arthropod has been detected, means for generating anaudio indicator corresponding to the detected arthropod, displaying animage of the detected arthropod, means for displaying instructionsassociated with how to remove the arthropod from the subject or thesurface, or means for displaying medical follow-up information.
 12. Thearthropod detection device of claim 9, wherein the plurality of objectdetection models are derived at least in part using crowd sourcingsolutions.